, Extremely large minibatch SGD: training resnet-50 on imagenet in 15 minutes, 2017.
, What is an object?, The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp.73-80, 2010.
, Measuring the objectness of image windows, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2189-2202, 2012.
, Augmented reality meets computer vision: Efficient data generation for urban driving scenes, International Journal of Computer Vision (IJCV), vol.126, issue.9, pp.961-972, 2018.
, A dataset for developing and benchmarking active vision, IEEE International Conference on Robotics and Automation (ICRA), cs.CV, 2017.
, Real-time pedestrian detection with deep network cascades, Proceedings of the British Machine Vision Conference, vol.2, p.4, 2015.
, Data augmentation generative adversarial networks. CoRR, 2017.
, Rank of experts: Detection network ensemble, 2017.
, SODMTGAN: Small Object Detection via Multi-Task Generative Adversarial Network, Computer Vision-ECCV 2018-15th European Conference, p.16, 2018.
, Zero-shot object detection. CoRR, abs/1804.04340, 2018.
, Relational inductive biases, deep learning, and graph networks, 2018.
, Self-taught object localization with deep networks, 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, pp.1-9, 2016.
DOI : 10.1109/wacv.2016.7477688
URL : http://arxiv.org/pdf/1409.3964
, A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification, 2017 IEEE International Conference On, 2017.
DOI : 10.1109/icra.2017.7989163
, Birdnet: a 3d object detection framework from lidar information, 2018.
, Pedestrian detection at 100 frames per second, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.2903-2910, 2012.
DOI : 10.1109/cvpr.2012.6248017
, Deep Learning for Logo Recognition, Neurocomputing, vol.245, pp.23-30, 2017.
DOI : 10.1016/j.neucom.2017.03.051
URL : http://arxiv.org/pdf/1701.02620
, Weakly Supervised Deep Detection Networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, 2016.
DOI : 10.1109/cvpr.2016.311
, Weakly supervised object detection with convex clustering, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
DOI : 10.1109/cvpr.2015.7298711
URL : https://lirias.kuleuven.be/bitstream/123456789/511404/1/3966_final_OA.pdf
, Fine-grained evaluation on face detection in the wild, Automatic Face and Gesture Recognition (FG), pp.1-7, 2015.
, Softnms-improving object detection with one line of code, IEEE International Conference on Computer Vision, pp.5562-5570, 2017.
DOI : 10.1109/iccv.2017.593
URL : http://arxiv.org/pdf/1704.04503
, Detecting people using mutually consistent poselet activations, Computer Vision-ECCV 2010, 11th European Conference on Computer Vision, pp.168-181, 2010.
DOI : 10.1007/978-3-642-15567-3_13
URL : http://www.cs.berkeley.edu/%7Esmaji/papers/bmbm-poselets-eccv10.pdf
, Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.95-104, 2017.
DOI : 10.1109/cvpr.2017.18
URL : http://arxiv.org/pdf/1612.05424
, StuffNet-Using 'Stuff' to Improve Object Detection, IEEE Winter Conf. on Applications of Computer Vision (WACV), 2017.
DOI : 10.1109/wacv.2017.109
URL : http://arxiv.org/pdf/1610.05861
, The eurocity persons dataset: A novel benchmark for object detection, 2018.
, Deep textspotter: An end-toend trainable scene text localization and recognition framework, IEEE International Conference on Computer Vision, pp.2223-2231, 2017.
DOI : 10.1109/iccv.2017.242
, A unified multi-scale deep convolutional neural network for fast object detection, Computer Vision-ECCV 2016-14th European Conference, pp.354-370, 2016.
DOI : 10.1007/978-3-319-46493-0_22
URL : http://arxiv.org/pdf/1607.07155
, Feature-fused SSD: fast detection for small objects, 2017.
, Constrained parametric min-cuts for automatic object segmentation, The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp.3241-3248, 2010.
DOI : 10.1109/cvpr.2010.5540063
URL : http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_carreira1.pdf
, Cpmc: Automatic object segmentation using constrained parametric min-cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.7, pp.1312-1328, 2011.
DOI : 10.1109/tpami.2011.231
, End-to-End Incremental Learning, Computer Vision-ECCV 2018-15th European Conference, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01849366
, Deep MANTA: A coarse-to-fine many-task network for joint 2d and 3d vehicle analysis from monocular image, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.1827-1836, 2017.
DOI : 10.1109/cvpr.2017.198
URL : https://hal.archives-ouvertes.fr/hal-01653519
, R-CNN for Small Object Detection, Computer Vision-ACCV 2016-13th Asian Conference on Computer Vision, vol.10115, pp.214-230, 2016.
DOI : 10.1007/978-3-319-54193-8_14
, Supervised transformer network for efficient face detection, Computer Vision-ECCV 2016-14th European Conference, 2016.
DOI : 10.1007/978-3-319-46454-1_8
URL : http://arxiv.org/pdf/1607.05477
, Detection evolution with multi-order contextual co-occurrence, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.1798-1805, 2013.
DOI : 10.1109/cvpr.2013.235
URL : http://www.eecis.udel.edu/~ding/files/cvpr13.pdf
, LSTD: A low-shot transfer detector for object detection, Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018.
, Discover and Learn New Objects from Documentaries, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.1111-1120, 2017.
DOI : 10.1109/cvpr.2017.124
URL : http://arxiv.org/pdf/1707.09593
, Optimizing video object detection via a scale-time lattice, 2018.
, Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, issue.4, pp.834-848, 2018.
DOI : 10.1109/tpami.2017.2699184
URL : http://arxiv.org/pdf/1606.00915
, Robust physical adversarial attack on faster R-CNN object detector, 2018.
, Monocular 3d object detection for autonomous driving, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
DOI : 10.1109/cvpr.2016.236
, Sanja Fidler, and Raquel Urtasun. 3d object proposals for accurate object class detection, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, pp.424-432, 2015.
, Improving object proposals with multi-thresholding straddling expansion, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
, Multi-view 3d object detection network for autonomous driving, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.6526-6534, 2017.
DOI : 10.1109/cvpr.2017.691
URL : http://arxiv.org/pdf/1611.07759
, Spatial Memory for Context Reasoning in Object Detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.
DOI : 10.1109/iccv.2017.440
URL : http://arxiv.org/pdf/1704.04224
, Domain adaptive faster R-CNN for object detection in the wild, 2018.
, Dual path networks, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp.4467-4475, 2017.
, Weaving multi-scale context for single shot detector, 2017.
, RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
DOI : 10.1109/cvpr.2016.315
, A Survey on Object Detection in Optical Remote Sensing Images, ISPRS Journal of Photogrammetry and Remote Sensing, vol.117, pp.11-28, 2016.
DOI : 10.1016/j.isprsjprs.2016.03.014
URL : https://manuscript.elsevier.com/S0924271616300144/pdf/S0924271616300144.pdf
, Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.12, pp.7405-7415, 2016.
DOI : 10.1109/tgrs.2016.2601622
, Long short-term memorynetworks for machine reading, Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp.551-561, 2016.
, Bing: Binarized normed gradients for objectness estimation at 300fps, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.3286-3293, 2014.
, Xception: Deep learning with depthwise separable convolutions, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.1800-1807, 2017.
, The cityscapes dataset for semantic urban scene understanding, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
, A comprehensive survey on domain adaptation for visual applications, Advances in Computer Vision and Pattern Recognition, pp.1-35, 2017.
, Autoaugment: Learning augmentation policies from data, 2018.
, Instance-aware semantic segmentation via multi-task network cascades, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.3150-3158, 2016.
, R-fcn: Object detection via region-based fully convolutional networks, Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems, pp.379-387, 2016.
, Deformable convolutional networks, IEEE International Conference on Computer Vision, pp.764-773, 2017.
, Histograms of oriented gradients for human detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005, vol.1, pp.886-893, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512
, text detection with convolutional neural networks, International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAP), pp.290-294, 2008.
, Imagenet: A large-scale hierarchical image database, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.248-255, 2009.
, Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled RegionBased Convolutional Neural Networks, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.10, pp.3652-3664, 2017.
, Amodal Detection of 3D Objects: Inferring 3D Bounding Boxes from 2D Ones in RGB-Depth Images, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.398-406, 2017.
, Dataset augmentation in feature space, CoRR, 2017.
, Improved regularization of convolutional neural networks with cutout, 2017.
, Pedestrian detection: An evaluation of the state of the art, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.4, pp.743-761, 2012.
, Few-shot object detection. CoRR, abs/1706.08249, 2017.
, MANTRA: Minimum Maximum Latent Structural SVM for Image Classification and Ranking, IEEE International Conference on Computer Vision, ICCV 2015, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01343784
, Weldon: Weakly supervised learning of deep convolutional neural networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01343785
, WILDCAT: Weakly Supervised Learning of Deep ConvNets for Image Classification, Pointwise Localization and Segmentation, 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.
DOI : 10.1109/cvpr.2017.631
URL : https://hal.archives-ouvertes.fr/hal-01515640
, Modeling Visual Context is Key to Augmenting Object Detection Datasets, Computer VisionECCV 2018-15th European Conference, p.18, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01844474
, Synthesizing scenes for instance detection, 2017.
, Cut, paste and learn: Surprisingly easy synthesis for instance detection, IEEE International Conference on Computer Vision, pp.1310-1319, 2017.
DOI : 10.1109/iccv.2017.146
URL : http://arxiv.org/pdf/1708.01642
, Improving small object proposals for company logo detection, Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, pp.167-174, 2017.
DOI : 10.1145/3078971.3078990
URL : http://arxiv.org/pdf/1704.08881
, Category independent object proposals, Computer Vision-ECCV 2010, 11th European Conference on Computer Vision, pp.575-588, 2010.
DOI : 10.1007/978-3-642-15555-0_42
URL : http://www-2.cs.cmu.edu/%7Edhoiem/publications/eccv2010_CategoryIndependentProposals_ian.pdf
, Category-independent object proposals with diverse ranking, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.2, pp.222-234, 2014.
DOI : 10.1109/tpami.2013.122
URL : http://web.engr.illinois.edu/~iendres2/publications/pami2013_proposals_preprint.pdf
, Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks, IEEE International Conference on Robotics and Automation (ICRA), 2017.
DOI : 10.1109/icra.2017.7989161
URL : http://arxiv.org/pdf/1609.06666
, Monocular pedestrian detection: Survey and experiments, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.12, pp.2179-2195, 2008.
DOI : 10.1109/tpami.2008.260
URL : http://gavrila.net/pami09.pdf
, A multilevel mixture-of-experts framework for pedestrian classification, IEEE Transactions on Image Processing, vol.20, issue.10, pp.2967-2979, 2011.
DOI : 10.1109/tip.2011.2142006
URL : http://www.gavrila.net/tip11.pdf
, Scalable Object Detection Using Deep Neural Networks, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, 2014.
DOI : 10.1109/cvpr.2014.276
URL : http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf
, Depth and appearance for mobile scene analysis, IEEE 11th International Conference on Computer Vision, ICCV, pp.1-8, 2007.
DOI : 10.1109/iccv.2007.4409092
, The pascal visual object classes (voc) challenge. International Journal of Computer Vision (IJCV), vol.88, pp.303-338, 2010.
DOI : 10.1007/s11263-009-0275-4
, Detect to track and track to detect, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.3038-3046, 2017.
DOI : 10.1109/iccv.2017.330
URL : http://arxiv.org/pdf/1710.03958
, Object detection with discriminatively trained part-based models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.9, pp.1627-1645, 2010.
DOI : 10.1109/tpami.2009.167
URL : http://people.cs.uchicago.edu/~pff/papers/lsvm-pami.pdf
, Interpretable explanations of black boxes by meaningful perturbation, IEEE International Conference on Computer Vision, pp.3449-3457, 2017.
, DSSD : Deconvolutional single shot detector. CoRR, abs/1701.06659, 2017.
, Recent advances in zero-shot recognition: Toward data-efficient understanding of visual content, IEEE Signal Processing Magazine, vol.35, issue.1, pp.112-125, 2018.
, Virtual worlds as proxy for multiobject tracking analysis, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
, Dynamic zoom-in network for fast object detection in large images, 2017.
, A neural architecture for fast and robust face detection, Pattern Recognition, 2002. Proceedings. 16th International Conference on, vol.2, pp.44-47, 2002.
, Multi-evidence filtering and fusion for multi-label classification, object detection and semantic segmentation based on weakly supervised learning, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Are we ready for autonomous driving? the kitti vision benchmark suite, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3354-3361, 1621.
, Synthesizing training data for object detection in indoor scenes, Robotics: Science and Systems XIII, Massachusetts Institute of Technology, 2017.
, Adaptive image sampling and windows classification for on-board pedestrian detection, Proceedings of the 5th International Conference on Computer Vision Systems (ICVS 2007), 2007.
, Attend Refine Repeat-Active Box Proposal Generation via In-Out Localization, Proceedings of the British Machine Vision Conference, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01832771
, Object detection via a multi-region and semantic segmentation-aware cnn model, IEEE Conference on Computer Vision and Pattern Recognition, pp.1134-1142, 2015.
, LocNet: Improving Localization Accuracy for Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01832507
, Fast r-cnn, IEEE International Conference on Computer Vision, ICCV 2015, pp.1440-1448, 2015.
, Rich feature hierarchies for accurate object detection and semantic segmentation, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.580-587, 2014.
, Deformable part models are convolutional neural networks, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
, Objects as context for part detection, 2017.
, Generative adversarial nets, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, pp.2672-2680, 2014.
, Maxout networks, Proceedings of the 30th International Conference on Machine Learning, pp.16-21, 2013.
, Yangqing Jia, and Kaiming He. Accurate, large minibatch SGD: training imagenet in 1 hour. CoRR, abs/1706.02677, 2017.
, Synthetic Data for Text Localisation in Natural Images, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.2315-2324, 2016.
, Exploring person context and local scene context for object detection. CoRR, abs/1511.08177, 2015.
, Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding, 2015.
, Seq-nms for video object detection, 2016.
, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.37, issue.9, pp.1904-1916, 2015.
, Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016.
, Mask r-cnn, IEEE International Conference on Computer Vision, pp.2980-2988, 2017.
, An end-to-end textspotter with explicit alignment and attention, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Learning Spatial Context-Using Stuff to Find Things, Computer Vision-ECCV 2008, 10th European Conference on Computer Vision, 2008.
, End-to-end training of object class detectors for mean average precision, Computer Vision-ACCV 2016-13th
, Asian Conference on Computer Vision, pp.198-213, 2016.
, Warped Convolutions-Efficient Invariance to Spatial Transformations, International Conference on Machine Learning (ICML), 2017.
, Impression network for video object detection. CoRR, abs/1712.05896, 2017.
, Lidar-based 3d object perception, Proceedings of 1st international workshop on cognition for technical systems, vol.1, 2008.
, On pre-trained image features and synthetic images for deep learning, 2017.
, Face Detection: A Survey. Computer Vision and Image Understanding (CVIU), vol.83, pp.236-274, 2001.
, Lsda: Large scale detection through adaptation, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, pp.3536-3544, 2014.
, Detector discovery in the wild: Joint multiple instance and representation learning, IEEE Conference on Computer Vision and Pattern Recognition, pp.2883-2891, 2015.
, Diagnosing error in object detectors, Computer Vision-ECCV 2012-12th European Conference on Computer Vision, pp.340-353, 2012.
, A convnet for non-maximum suppression, German Conference on Pattern Recognition, pp.192-204, 2016.
, How good are detection proposals, really, British Machine Vision Conference, 2014.
, Learning nonmaximum suppression, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.6469-6477, 2017.
, Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark, International Joint Conference on Neural Networks, p.1288, 2013.
, Mobilenets: Efficient convolutional neural networks for mobile vision applications, 2017.
, Relation networks for object detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Squeeze-and-excitation networks. CoRR, 2017.
, Finding tiny faces, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.1522-1530, 2017.
DOI : 10.1109/cvpr.2017.166
, Condensenet: An efficient densenet using learned group convolutions, 2017.
, Densely connected convolutional networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition, vol.1, p.3, 2017.
DOI : 10.1109/cvpr.2017.243
URL : http://arxiv.org/pdf/1608.06993
, Speed/accuracy trade-offs for modern convolutional object detectors, 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.
DOI : 10.1109/cvpr.2017.351
URL : http://arxiv.org/pdf/1611.10012
, Learning to prune filters in convolutional neural networks, 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, pp.709-718, 2018.
DOI : 10.1109/wacv.2018.00083
URL : http://arxiv.org/pdf/1801.07365
, Multimodal unsupervised image-to-image translation, 2018.
, Binarized neural networks, Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems, pp.4107-4115, 2016.
, Quantized neural networks: Training neural networks with low precision weights and activations, The Journal of Machine Learning Research, vol.18, issue.1, pp.6869-6898, 2017.
, Rigor: Reusing inference in graph cuts for generating object regions, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.336-343, 2014.
DOI : 10.1109/cvpr.2014.50
URL : http://cpl.cc.gatech.edu/projects/RIGOR/pubs/humayun_CVPR_2014_rigor.pdf
, Deep learning for class-generic object detection. CoRR, abs/1312, vol.6885, 2013.
, Densenet: Implementing efficient convnet descriptor pyramids. CoRR, abs/1404.1869, 2014.
, Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size. CoRR, abs/1602.07360v3, 2016.
, Data augmentation by pairing samples for images classification, 2018.
, Cross-domain weakly-supervised object detection through progressive domain adaptation. CoRR, abs/1803.11365, 2018.
, Batch renormalization: Towards reducing minibatch dependence in batch-normalized models, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp.1942-1950, 2017.
, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, pp.448-456, 2015.
, Synthetic data and artificial neural networks for natural scene text recognition. CoRR, abs/1406.2227, 2014.
, Spatial transformer networks, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, 2015.
, FDDB: A Benchmark for Face Detection in Unconstrained Settings, 2010.
, Enhancement of SSD by concatenating feature maps for object detection. CoRR, abs/1705.09587, 2017.
, Towards improved cartoon face detection and recognition systems, CoRR, 2018.
, Acquisition of localization confidence for accurate object detection, 2018.
, R2CNN: rotational region CNN for orientation robust scene text detection. CoRR, abs/1706.09579, 2017.
, Logo retrieval with a contrario visual query expansion, Wen Gao, Yong Rui
DOI : 10.1145/1631272.1631361
, Proceedings of the 17th International Conference on Multimedia, pp.581-584, 2009.
, A Survey on Moving Object Detection and Tracking in Video Surveillance System, International Journal of Soft Computing and Engineering (IJSCE), vol.2, issue.3, 2012.
, Datadriven objectness, IEEE Transactions on Pattern Analysis and Machine Intelligence, issue.1, pp.189-195, 2015.
DOI : 10.1109/tpami.2014.2315811
, Object detection from video tubelets with convolutional neural networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.817-825, 2016.
DOI : 10.1109/cvpr.2016.95
URL : http://arxiv.org/pdf/1604.04053
, Tubelets with Convolutional Neural Networks for Object Detection from Videos. IEEE Transactions on Circuits and Systems for Video Technology, pp.1-1, 2017.
DOI : 10.1109/tcsvt.2017.2736553
URL : http://arxiv.org/pdf/1604.02532
, 13th International Conference on Document Analysis and Recognition (ICDAR), pp.1156-1160, 2015.
, Progressive growing of GANs for improved quality, stability, and variation, International Conference on Learning Representations, 2018.
, Object detection can be improved using human-derived contextual expectations, 2016.
, Weakly supervised one-shot detection with attention siamese networks, 2018.
, Undoing the damage of dataset bias, Computer Vision-ECCV 2012-12th European Conference on Computer Vision, pp.158-171, 2012.
DOI : 10.1007/978-3-642-33718-5_12
URL : http://people.csail.mit.edu/tomasz/papers/khosla_eccv2012.pdf
, PVANET: deep but lightweight neural networks for real-time object detection, 2016.
, Adam: A method for stochastic optimization. CoRR, abs/1412, vol.6980, 2014.
, Pushing the frontiers of unconstrained face detection and recognition: Iarpa janus benchmark a, IEEE Conference on Computer Vision and Pattern Recognition, pp.1931-1939, 2015.
, Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.5454-5463, 2017.
DOI : 10.1109/cvpr.2017.579
URL : http://arxiv.org/pdf/1609.02132
, HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
DOI : 10.1109/cvpr.2016.98
URL : http://arxiv.org/pdf/1604.00600
, RON: reverse connection with objectness prior networks for object detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.5244-5252, 2017.
, Deep feature pyramid reconfiguration for object detection. CoRR, abs/1808.07993, 2018.
, Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization, First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies, pp.2144-2151, 2011.
, Openimages: A public dataset for large-scale multi-label and multi-class image classification, 2017.
, Visual genome: Connecting language and vision using crowdsourced dense image annotations, International Journal of Computer Vision (IJCV), vol.123, issue.1, pp.32-73, 2017.
, One weird trick for parallelizing convolutional neural networks. CoRR, abs/1404, vol.5997, 2014.
, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems, pp.1106-1114, 2012.
, Joint 3d proposal generation and object detection from view aggregation, 2017.
, Track and transfer: Watching videos to simulate strong human supervision for weakly-supervised object detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp.3548-3556, 2016.
, Deepbox: Learning objectness with convolutional networks, IEEE International Conference on Computer Vision, ICCV 2015, pp.2479-2487, 2015.
, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, Proceedings of the Eighteenth International Conference on Machine Learning, ICML '01, pp.282-289, 2001.
, xview: Objects in context in overhead imagery, 2018.
, Beyond sliding windows: Object localization by efficient subwindow search, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR, pp.24-26, 2008.
, TI-POOLING: transformation-invariant pooling for feature learning in convolutional neural networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.289-297, 2016.
, Cornernet: Detecting objects as paired keypoints, Computer Vision-ECCV 2018-15th European Conference, 2018.
, Efficient backprop, Neural Networks: Tricks of the Trade-Second Edition, pp.9-48, 2012.
, Multi-class multi-object tracking using changing point detection, Computer Vision-ECCV 2016-14th European Conference, vol.9914, pp.68-83, 2016.
, Residual features and unified prediction network for single stage detection, 2017.
, Wideresidual-inception networks for real-time object detection, Intelligent Vehicles Symposium (IV), 2017 IEEE, pp.758-764, 2017.
, Smart augmentation learning an optimal data augmentation strategy, IEEE Access, vol.5, pp.5858-5869, 2017.
, 3D Fully Convolutional Network for Vehicle Detection in Point Cloud, IROS, 2017.
, Object detection via end-to-end integration of aspect ratio and context aware part-based models and fully convolutional networks, 2016.
, Vehicle detection from 3d lidar using fully convolutional network, Robotics: Science and Systems XII, 2016.
, A convolutional neural network cascade for face detection, IEEE Conference on Computer Vision and Pattern Recognition, pp.5325-5334, 2015.
, Zoom out-and-in network with recursive training for object proposal, 2017.
, Scale-aware fast r-cnn for pedestrian detection, IEEE Transactions on Multimedia, 2017.
, Perceptual generative adversarial networks for small object detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.1951-1959, 2017.
, A new benchmark for vision-based cyclist detection, Intelligent Vehicles Symposium (IV), pp.1028-1033, 2016.
, Fully convolutional instance-aware semantic segmentation, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.4438-4446, 2017.
, Scene graph generation from objects, phrases and caption regions, 2017.
, Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages, Proceedings of the British Machine Vision Conference, 2018.
, Light-head R-CNN: in defense of two-stage object detector, 2017.
, R-FCN++: Towards Accurate Region-Based Fully Convolutional Networks for Object Detection, AAAI, p.8, 2018.
, Detnet: A backbone network for object detection, 2018.
, Learning without Forgetting, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.
, FSSD: feature fusion single shot multibox detector, 2017.
, Textboxes++: A single-shot oriented scene text detector. CoRR, abs/1801.02765, 2018.
, Rotationsensitive regression for oriented scene text detection, 2018.
, Mutual Enhancement for Detection of Multiple Logos in Sports Videos, IEEE International Conference on Computer Vision, pp.4856-4865, 2017.
, Microsoft coco: Common objects in context, Computer Vision-ECCV 2014-13th European Conference, pp.740-755, 2014.
, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition, vol.1, p.4, 2017.
, Kaiming He, and Piotr Dollár. Focal loss for dense object detection, IEEE International Conference on Computer Vision, pp.2999-3007, 2017.
, Hierarchical part-template matching for human detection and segmentation, IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/iccv.2007.4408975
, Neural networks with few multiplications, 2015.
, Fast multiclass vehicle detection on aerial images, IEEE Geoscience and Remote Sensing Letters, vol.12, issue.9, pp.1938-1942, 2015.
, Ssd: Single shot multibox detector, Computer Vision-ECCV 2016-14th European Conference, pp.21-37, 2016.
DOI : 10.1007/978-3-319-46448-0_2
URL : http://arxiv.org/pdf/1512.02325
, Deep matching prior network: Toward tighter multi-oriented text detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.3454-3461, 2017.
DOI : 10.1109/cvpr.2017.368
URL : http://arxiv.org/pdf/1703.01425
, A high resolution optical satellite image dataset for ship recognition and some new baselines, ICPRAM, pp.324-331, 2017.
DOI : 10.5220/0006120603240331
, The proceedings of the seventh IEEE international conference on, Computer vision, vol.2, pp.1150-1157, 1999.
, Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision (IJCV), vol.60, issue.2, pp.91-110, 2004.
, Standard detectors aren't (currently) fooled by physical adversarial stop signs, 2017.
, Icdar 2003 robust reading competitions, Seventh International Conference on Document Analysis and Recognition, pp.682-687, 2003.
DOI : 10.1109/icdar.2003.1227749
URL : https://hal.archives-ouvertes.fr/hal-01527429
, Arbitrary-Oriented Scene Text Detection via Rotation Proposals, IEEE Transactions on Multimedia, pp.1-1, 2018.
DOI : 10.1109/tmm.2018.2818020
URL : http://arxiv.org/pdf/1703.01086
, Prime object proposals with randomized prim's algorithm, IEEE International Conference on Computer Vision, ICCV 2013, pp.2536-2543, 2013.
DOI : 10.1109/iccv.2013.315
URL : https://lirias.kuleuven.be/bitstream/123456789/450464/1/3716_final_OA.pdf
, What can help pedestrian detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.6034-6043, 2017.
DOI : 10.1109/cvpr.2017.639
URL : http://arxiv.org/pdf/1705.02757
, Performance evaluation of object detection algorithms, International Conference on Pattern Recognition (ICPR), vol.3, pp.965-969, 2002.
DOI : 10.1109/icpr.2002.1048198
, A framework for multiple-instance learning, Advances in Neural Information Processing Systems, vol.10, pp.570-576, 1997.
, On-the-fly network pruning for object detection, 2016.
, Domain-adaptive deep network compression, IEEE International Conference on Computer Vision, pp.4299-4307, 2017.
DOI : 10.1109/iccv.2017.460
URL : http://arxiv.org/pdf/1709.01041
, Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views, IEEE Conference on Computer Vision and Pattern Recognition, pp.6024-6033, 2016.
DOI : 10.1109/cvpr.2016.648
URL : https://hal.archives-ouvertes.fr/hal-01801049
, Reading handwritten digits: A zip code recognition system, IEEE Computer, vol.25, issue.7, pp.59-63, 1992.
DOI : 10.1109/2.144441
URL : http://oro.open.ac.uk/35664/1/matan-92.pdf
, IARPA Janus Benchmark-C: Face Dataset and Protocol, ICB, p.8, 2018.
DOI : 10.1109/icb2018.2018.00033
, Scenenet RGB-D: can 5m synthetic images beat generic imagenet pre-training on indoor segmentation, IEEE International Conference on Computer Vision, pp.2697-2706, 2017.
, Lmnet: Real-time multiclass object detection on CPU using 3d lidar, 2018.
, IIIT-CFW: A Benchmark Database of Cartoon Faces in the Wild, 2016.
, Scene text recognition using higher order language priors, British Machine Vision Conference, BMVC 2012, 2012.
DOI : 10.5244/c.26.127
URL : https://hal.archives-ouvertes.fr/hal-00818183
, Watch and learn: Semi-supervised learning of object detectors from videos, IEEE Conference on Computer Vision and Pattern Recognition, pp.3593-3602, 2015.
DOI : 10.1109/cvpr.2015.7298982
URL : http://arxiv.org/pdf/1505.05769
, Physics-aware Self-supervised Training of CNNs for Object Detection, IEEE International Conference on Robotics and Automation (ICRA), 2017.
, Never-Ending Learning, Commun. ACM, vol.61, issue.5, pp.103-115, 2018.
DOI : 10.1037/e660332010-001
, Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey, IEEE Transactions on Intelligent Transportation Systems, vol.13, pp.1484-1497, 2012.
DOI : 10.1109/tits.2012.2209421
URL : http://cvrr.ucsd.edu/publications/2012/Mogelmose_ITS2012.pdf
, Deformable Part-based Fully Convolutional Network for Object Detection, Proceedings of the British Machine Vision Conference, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01637070
, 3d bounding box estimation using deep learning and geometry, 2017 IEEE Conference on Computer Vision and Pattern Recognition, vol.2017, pp.5632-5640, 2017.
DOI : 10.1109/cvpr.2017.597
URL : http://arxiv.org/pdf/1612.00496
, Spatial semantic regularisation for large scale object detection, IEEE International Conference on Computer Vision, ICCV 2015, 2003.
DOI : 10.1109/iccv.2015.232
URL : http://arxiv.org/pdf/1510.02949
, Generative adversarial network based acoustic scene training set augmentation and selection using svm hyper-plane, Proc. DCASE, pp.93-97, 2017.
, A large contextual dataset for classification, detection and counting of cars with deep learning, Computer Vision-ECCV 2016-14th European Conference, pp.785-800, 2016.
, Pushing the limits of unconstrained face detection: a challenge dataset and baseline results. CoRR, abs/1804.10275, 2018.
, An Iterative Grid Based Object Detector, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
DOI : 10.1109/cvpr.2016.260
URL : http://arxiv.org/pdf/1512.07729
, SSH: Single Stage Headless Face Detector, IEEE International Conference on Computer Vision, 2017.
DOI : 10.1109/iccv.2017.522
URL : http://arxiv.org/pdf/1708.03979
, Stacked hourglass networks for human pose estimation, Computer Vision-ECCV 2016-14th European Conference, pp.483-499, 2016.
DOI : 10.1007/978-3-319-46484-8_29
URL : http://arxiv.org/pdf/1603.06937
, Learning convolutional neural networks for graphs, International conference on machine learning, pp.2014-2023, 2016.
, A convolutional neural network hand tracker, Advances in Neural Information Processing Systems 8, NIPS, pp.901-908, 1995.
, ON THE USE OF DEEP NEURAL NETWORKS FOR THE DETECTION OF SMALL VEHICLES IN ORTHOIMAGES, IEEE International Conference on Image Processing, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01527906
, Localization Recall Precision (LRP): A New Performance Metric for Object Detection, Computer Vision-ECCV 2018-15th European Conference, 2018.
, Weakly supervised object recognition with convolutional neural networks, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, 2014.
, Is object localization for free?-weakly-supervised learning with convolutional neural networks, IEEE Conference on Computer Vision and Pattern Recognition, pp.685-694, 2015.
DOI : 10.1109/cvpr.2015.7298668
URL : https://hal.archives-ouvertes.fr/hal-01015140
, Synergistic face detection and pose estimation with energy-based models, Journal of Machine Learning Research, vol.8, pp.1197-1215, 2007.
DOI : 10.1007/11957959_10
, Factors in finetuning deep model for object detection with long-tail distribution, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
DOI : 10.1109/cvpr.2016.100
, Joint deep learning for pedestrian detection, IEEE International Conference on Computer Vision, ICCV 2013, 2013.
DOI : 10.1109/iccv.2013.257
URL : http://www.ee.cuhk.edu.hk/%7Exgwang/papers/ouyangWiccv13.pdf
, Single-pedestrian detection aided by multipedestrian detection, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3198-3205, 2013.
DOI : 10.1109/cvpr.2013.411
, DeepID-Net: Deformable deep convolutional neural networks for object detection, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, 2015.
DOI : 10.1109/cvpr.2015.7298854
URL : http://arxiv.org/pdf/1412.5661
, Learning chained deep features and classifiers for cascade in object detection, 2017.
, Pedestriansynthesis-gan: Generating pedestrian data in real scene and beyond, 2018.
, We don't need no bounding-boxes: Training object class detectors using only human verification, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
, Training object class detectors with click supervision, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.180-189, 2017.
, A trainable system for object detection, International Journal of Computer Vision (IJCV), vol.38, issue.1, pp.15-33, 2000.
, Extreme network compression via filter group approximation. CoRR, abs/1807.11254, 2018.
, Megdet: A large mini-batch object detector, 2017.
, Large kernel matters???improve semantic segmentation by global convolutional network, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.1743-1751, 2017.
DOI : 10.1109/cvpr.2017.189
URL : http://arxiv.org/pdf/1703.02719
, Synthetic to real adaptation with generative correlation alignment networks, 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, pp.1982-1991, 2018.
DOI : 10.1109/wacv.2018.00219
URL : http://arxiv.org/pdf/1701.05524
, Learning Deep Object Detectors from 3D Models, IEEE International Conference on Computer Vision, ICCV 2015, 2015.
DOI : 10.1109/iccv.2015.151
URL : http://arxiv.org/pdf/1412.7122
, View-based and modular eigenspaces for face recognition, Conference on Computer Vision and Pattern Recognition, pp.84-91, 1994.
DOI : 10.1109/cvpr.1994.323814
, What is holding back convnets for detection, German Conference on Pattern Recognition, pp.517-528, 2015.
DOI : 10.1007/978-3-319-24947-6_43
URL : https://doi.org/10.1007/978-3-319-24947-6_43
, The effectiveness of data augmentation in image classification using deep learning. CoRR, abs/1712.04621, 2017.
, Evaluation of Deep Models for Real-Time Small Object Detection, ICONIP, vol.10636, pp.516-526, 2017.
DOI : 10.1007/978-3-319-70090-8_53
, Learning to segment object candidates, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, 1990.
, From Image-level to Pixel-level Labeling with Convolutional Networks, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
DOI : 10.1109/cvpr.2015.7298780
URL : http://arxiv.org/pdf/1411.6228
, Learning to refine object segments, Computer Vision-ECCV 2016-14th European Conference, pp.75-91, 2016.
, A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection, IEEE Conference on Computer and Robot Vision, 2018.
, Multiscale combinatorial grouping for image segmentation and object proposal generation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.1, pp.128-140, 2017.
DOI : 10.1109/tpami.2016.2537320
URL : http://upcommons.upc.edu/bitstream/2117/105287/1/1503.00848.pdf
, Integral histogram: A fast way to extract histograms in cartesian spaces, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005, pp.829-836, 2005.
, Pointnet: Deep learning on point sets for 3d classification and segmentation, 2017 IEEE Conference on Computer Vision and Pattern Recognition, vol.2017, 2017.
, Frustum pointnets for 3d object detection from RGB-D data, 2017.
, Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp.5105-5114, 2017.
, Unrealcv: Connecting computer vision to unreal engine, Computer Vision-ECCV 2016-14th European Conference, vol.9915, pp.909-916, 2016.
, Zero-shot object detection: Learning to simultaneously recognize and localize novel concepts, 2018.
, Learning a category independent object detection cascade, IEEE International Conference on Computer Vision, pp.1052-1059, 2011.
DOI : 10.1109/iccv.2011.6126351
URL : https://hal.archives-ouvertes.fr/hal-00855735
, Subspace Alignment Based Domain Adaptation for RCNN Detector, Proceedings of the British Machine Vision Conference, vol.11, pp.166-167, 2015.
DOI : 10.5244/c.29.166
URL : http://www.bmva.org/bmvc/2015/papers/paper166/abstract166.pdf
, RefineNet: Iterative refinement for accurate object localization, IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp.1528-1533, 2016.
, Object detection using deep cnns trained on synthetic images. CoRR, abs/1706.06782, 2017.
, A deep pyramid deformable part model for face detection, IEEE 7th International Conference on Biometrics Theory, Applications and Systems, pp.1-8, 2015.
DOI : 10.1109/btas.2015.7358755
URL : http://arxiv.org/pdf/1508.04389
, Generating object segmentation proposals using global and local search, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.2417-2424, 2014.
DOI : 10.1109/cvpr.2014.310
, Xnor-net: Imagenet classification using binary convolutional neural networks, Computer Vision-ECCV 2016-14th European Conference, pp.525-542, 2016.
DOI : 10.1007/978-3-319-46493-0_32
URL : http://arxiv.org/pdf/1603.05279
, Learning to compose domain-specific transformations for data augmentation, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp.3236-3246, 2017.
, Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background, 2017.
, Vehicle detection in aerial imagery: A small target detection benchmark, Journal of Visual Communication and Image Representation, vol.34, pp.187-203, 2016.
DOI : 10.1016/j.jvcir.2015.11.002
URL : https://hal.archives-ouvertes.fr/hal-01122605
, Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.7464-7473, 2017.
DOI : 10.1109/cvpr.2017.789
URL : http://arxiv.org/pdf/1702.00824
, On the convergence of adam and beyond, International Conference on Learning Representations (ICLR), 2018.
, Real-time grasp detection using convolutional neural networks, IEEE International Conference on Robotics and Automation (ICRA), 2015.
DOI : 10.1109/icra.2015.7139361
URL : http://arxiv.org/pdf/1412.3128
, YOLO9000: better, faster, stronger, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.6517-6525, 2017.
DOI : 10.1109/cvpr.2017.690
URL : http://arxiv.org/pdf/1612.08242
, Yolov3: An incremental improvement. CoRR, 2018.
, You only look once: Unified, real-time object detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.779-788, 2016.
DOI : 10.1109/cvpr.2016.91
URL : http://arxiv.org/pdf/1506.02640
, Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems, pp.91-99, 2015.
, Object detection networks on convolutional feature maps, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.7, pp.1476-1481, 2017.
, Weakly supervised localization of novel objects using appearance transfer, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
DOI : 10.1109/cvpr.2015.7299060
URL : http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Rochan_Weakly_Supervised_Localization_2015_CVPR_paper.pdf
, Densityaware person detection and tracking in crowds, IEEE International Conference on Computer Vision, ICCV 2011, pp.2423-2430, 2011.
DOI : 10.1109/iccv.2011.6126526
URL : https://hal.archives-ouvertes.fr/hal-00654266
, Scalable logo recognition in real-world images, Proceedings of the 1st ACM International Conference on Multimedia Retrieval, p.25, 2011.
DOI : 10.1145/1991996.1992021
, The elephant in the room, CoRR, 2018.
, Non-maximum suppression for object detection by passing messages between windows, Computer Vision-ACCV 2014-12th Asian Conference on Computer Vision, pp.290-306, 2014.
, Active learning with version spaces for object detection. CoRR, abs/1611.07285, 2016.
, Optimized pedestrian detection for multiple and occluded people, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3690-3697, 2013.
DOI : 10.1109/cvpr.2013.473
URL : http://www.cse.psu.edu/%7Ercollins/Papers/cvpr2013SitapaCollins.pdf
, Learning internal representations by error propagation, 1985.
, ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV), vol.115, issue.3, pp.211-252, 2015.
DOI : 10.1007/s11263-015-0816-y
URL : http://dspace.mit.edu/bitstream/1721.1/104944/1/11263_2015_Article_816.pdf
, Detecting pedestrians by learning shapelet features, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp.18-23, 2007.
DOI : 10.1109/cvpr.2007.383134
URL : http://www.cs.sfu.ca/%7Emori/research/papers/sabzmeydani_shapelet_cvpr07.pdf
, Recognition using visual phrases, The 24th IEEE Conference on Computer Vision and Pattern Recognition, pp.1745-1752, 2011.
DOI : 10.1109/cvpr.2011.5995711
URL : http://www.cs.rit.edu/%7Erlc/Courses/ImageUnderstanding/Papers/Current/visualPhrases.pdf
, 30hz object detection with DPM V5, Computer Vision-ECCV 2014-13th European Conference, vol.8689, pp.65-79, 2014.
DOI : 10.1007/978-3-319-10590-1_5
, Deep multimodal vehicle detection in aerial ISR imagery, 2017 IEEE Winter Conference on Applications of Computer Vision, pp.916-923, 2017.
, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, pp.4510-4520, 2018.
, Deformable part models with cnn features, SAICSIT Conf, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01109290
, Object detection using the statistics of parts, International Journal of Computer Vision (IJCV), vol.56, issue.3, pp.151-177, 2004.
DOI : 10.1023/b:visi.0000011202.85607.00
URL : http://www.cs.cmu.edu/~efros/courses/AP06/Papers/schneiderman-ijcv-04.pdf
, Overfeat: Integrated recognition, localization and detection using convolutional networks, 2013.
, Pedestrian detection with unsupervised multi-stage feature learning, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.3626-3633, 2013.
DOI : 10.1109/cvpr.2013.465
URL : http://arxiv.org/pdf/1212.0142
, Fast YOLO: A fast you only look once system for real-time embedded object detection in video, 2017.
, Generative adversarial learning towards fast weakly supervised detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Dsod: Learning deeply supervised object detectors from scratch, IEEE International Conference on Computer Vision, vol.3, 2017.
DOI : 10.1109/iccv.2017.212
URL : http://arxiv.org/pdf/1708.01241
, Learning object detectors from scratch with gated recurrent feature pyramids
DOI : 10.1109/iccv.2017.212
URL : http://arxiv.org/pdf/1708.01241
, , 2017.
, Detecting oriented text in natural images by linking segments, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.3482-3490, 2017.
, , 2017.
, Realtime rotation-invariant face detection with progressive calibration networks, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Incremental learning of object detectors without catastrophic forgetting, IEEE International Conference on Computer Vision, pp.3420-3429, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01573623
, Training region-based object detectors with online hard example mining, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.761-769, 2016.
DOI : 10.1109/cvpr.2016.89
URL : http://arxiv.org/pdf/1604.03540
, Beyond skip connections: Top-down modulation for object detection, 2016.
, Learning from Simulated and Unsupervised Images through Adversarial Training, IEEE Conference on Computer Vision and Pattern Recognition, pp.2242-2251, 2017.
DOI : 10.1109/cvpr.2017.241
URL : http://arxiv.org/pdf/1612.07828
, Vision-based pedestrian detection for rear-view cameras, Intelligent Vehicles Symposium Proceedings, pp.853-860, 2014.
DOI : 10.1109/ivs.2014.6856399
, Lifelong Machine Learning Systems: Beyond Learning Algorithms, 2013 AAAI Spring Symposium, p.7, 2013.
, Complexyolo: Real-time 3d object detection on point clouds, 2018.
, Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014.
, Deep inside convolutional networks: Visualising image classification models and saliency maps. CoRR, abs/1312, vol.6034, 2013.
, An analysis of scale invariance in object detection-snip, 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.
, R-FCN-3000 at 30fps: Decoupling detection and classification, 2017.
, SNIPER: efficient multi-scale training, 2018.
, Rendergan: Generating realistic labeled data, Front. Robotics and AI, 2018.
DOI : 10.3389/frobt.2018.00066
URL : https://www.frontiersin.org/articles/10.3389/frobt.2018.00066/pdf
, Content-Based Image Retrieval at the End of the Early Years, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, issue.12, p.32, 2000.
, Deep learning based multi-category object detection in aerial images, SPIE Defense+ Security, 2017.
DOI : 10.1117/12.2262083
, Fast deep vehicle detection in aerial images, 2017 IEEE Winter Conference on Applications of Computer Vision, pp.311-319, 2017.
DOI : 10.1109/wacv.2017.41
, Multi feature deconvolutional faster R-CNN for precise vehicle detection in aerial imagery, 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, pp.635-642, 2018.
DOI : 10.1109/wacv.2018.00075
, On learning to localize objects with minimal supervision, Proceedings of the 31th International Conference on Machine Learning, vol.32, pp.1611-1619, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00996849
, Weaklysupervised discovery of visual pattern configurations, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, pp.1637-1645, 2014.
, Striving for simplicity: The all convolutional net. CoRR, abs/1412, vol.6806, 2014.
, Large scale novel object discovery in 3d, 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, pp.179-188, 2018.
DOI : 10.1109/wacv.2018.00026
URL : http://arxiv.org/pdf/1701.07046
, End-to-end people detection in crowded scenes, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp.2325-2333, 2016.
DOI : 10.1109/cvpr.2016.255
URL : http://arxiv.org/pdf/1506.04878
, WebLogo-2M: Scalable Logo Detection by Deep Learning from the Web, ICCB Workshops, pp.270-279, 2017.
DOI : 10.1109/iccvw.2017.41
, Deep Learning Logo Detection with Data Expansion by Synthesising Context, IEEE Winter Conf. on Applications of Computer Vision (WACV), pp.530-539, 2017.
DOI : 10.1109/wacv.2017.65
URL : http://arxiv.org/pdf/1612.09322
, Open Logo Detection Challenge, Proceedings of the British Machine Vision Conference, 2018.
, From virtual to reality: Fast adaptation of virtual object detectors to real domains, British Machine Vision Conference, vol.1, p.3, 2014.
DOI : 10.5244/c.28.82
URL : http://www.bmva.org/bmvc/2014/files/abstract062.pdf
, ProNet: Learning to Propose Object-Specific Boxes for Cascaded Neural Networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, 2016.
DOI : 10.1109/cvpr.2016.379
URL : http://arxiv.org/pdf/1511.03776
, Scalable, high-quality object detection. CoRR, abs/1412.1441, 2014.
, Going deeper with convolutions, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp.1-9, 2015.
DOI : 10.1109/cvpr.2015.7298594
URL : http://arxiv.org/pdf/1409.4842
, Rethinking the inception architecture for computer vision, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp.2818-2826, 2016.
DOI : 10.1109/cvpr.2016.308
URL : http://arxiv.org/pdf/1512.00567
, Inception-v4, inception-resnet and the impact of residual connections on learning, AAAI, vol.4, p.12, 2017.
, Platform-aware neural architecture search for mobile. CoRR, abs/1807.11626, 2018.
, Shifting Weights: Adapting Object Detectors from Image to Video, Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held, 2012.
, Multiple instance detection network with online instance classifier refinement, 2017 IEEE Conference on Computer Vision and Pattern Recognition, vol.2017, 2017.
DOI : 10.1109/cvpr.2017.326
URL : http://arxiv.org/pdf/1704.00138
, Detection and tracking of occluded people, International Journal of Computer Vision (IJCV), vol.110, issue.1, pp.58-69, 2014.
DOI : 10.5244/c.26.9
URL : http://www.bmva.org/bmvc/2012/BMVC/paper009/paper009.pdf
, Subgraph decomposition for multi-target tracking, IEEE Conference on Computer Vision and Pattern Recognition, pp.5033-5041, 2015.
DOI : 10.1109/cvpr.2015.7299138
, ArbitraryOriented Vehicle Detection in Aerial Imagery with Single Convolutional Neural Networks, Remote Sensing, vol.9, pp.1170-1187, 2017.
, Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining, Sensors, vol.17, pp.336-353, 2017.
DOI : 10.3390/s17020336
URL : http://www.mdpi.com/1424-8220/17/2/336/pdf
, Large Scale Semi-supervised Object Detection using Visual and Semantic Knowledge Transfer, 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016.
DOI : 10.1109/cvpr.2016.233
URL : https://hal.archives-ouvertes.fr/hal-01488579
, Overhead imagery research data set???an annotated data library & tools to aid in the development of computer vision algorithms, 2009 IEEE Applied Imagery Pattern Recognition Workshop (AIPR 2009), pp.1-8, 2009.
, Improving deep learning using generic data augmentation, 2017.
, Training and testing object detectors with virtual images, 2017.
, Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning, vol.4, pp.26-31, 2012.
, Multi-view traffic sign detection, recognition, and 3d localisation. Machine vision and applications, vol.25, pp.633-647, 2014.
, Domain Adaptation in Computer Vision Applications, Advances in Computer Vision and Pattern Recognition, pp.37-55, 2017.
, Unbiased look at dataset bias, The 24th IEEE Conference on Computer Vision and Pattern Recognition, pp.1521-1528, 2011.
, A bayesian data augmentation approach for learning deep models, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems, pp.2797-2806, 2017.
, Varun Jampani, Cem Anil, Thang To, Eric Cameracci, Shaad Boochoon, and Stan Birchfield. Training deep networks with synthetic data: Bridging the reality gap by domain randomization, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018.
, Falling things: A synthetic dataset for 3d object detection and pose estimation. CoRR, abs/1804.06534, 2018.
, Context matters: Refining object detection in video with recurrent neural networks, Proceedings of the British Machine Vision Conference, 2016.
, Auto-context and its application to high-level vision tasks and 3d brain image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.10, pp.1744-1757, 2010.
, Detecting texts of arbitrary orientations in natural images, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.1083-1090, 2012.
, Pedestrian detection via classification on riemannian manifolds, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.10, pp.1713-1727, 2008.
, Open set logo detection and retrieval, Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol.5, pp.284-292, 2018.
, Selective search for object recognition, International Journal of Computer Vision (IJCV), vol.104, issue.2, pp.154-171, 2013.
, Original approach for the localisation of objects in images, IEE Proceedings-Vision, Image and Signal Processing, vol.141, pp.245-250, 1994.
, Segmentation as selective search for object recognition, IEEE International Conference on Computer Vision, pp.1879-1886, 2011.
, The iNaturalist Species Classification and Detection Dataset, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Learning from synthetic humans, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.4627-4635, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01505711
, Belongie. Coco-text: Dataset and benchmark for text detection and recognition in natural images, 2016.
, Object localization in imagenet by looking out of the window, Proceedings of the British Machine Vision Conference, vol.12, pp.27-28, 2015.
, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision (IJCV), vol.63, issue.2, pp.153-161, 2005.
DOI : 10.1109/iccv.2003.1238422
URL : http://www.merl.com/papers/docs/TR2003-90.pdf
, New features and insights for pedestrian detection, The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp.1030-1037, 2010.
DOI : 10.1109/cvpr.2010.5540102
URL : http://www.jdl.ac.cn/project/faceId/paperreading/Paper/hyren_20100507.pdf
, Minentropy latent model for weakly supervised object detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, End-to-end integration of a convolutional network, deformable parts model and non-maximum suppression, IEEE Conference on Computer Vision and Pattern Recognition, pp.851-859, 2015.
, Weakly Supervised Object Localization with Latent Category Learning, Computer Vision-ECCV 2014-13th European Conference, 2014.
DOI : 10.1007/978-3-319-10599-4_28
, Word spotting in the wild, Computer Vision-ECCV 2010, 11th European Conference on Computer Vision, pp.591-604, 2010.
DOI : 10.1007/978-3-642-15549-9_43
, Evolving boxes for fast vehicle detection, ICME, pp.1135-1140, 2017.
DOI : 10.1109/icme.2017.8019461
URL : http://arxiv.org/pdf/1702.00254
, Pelee: A Real-Time Object Detection System on Mobile Devices, International Conference on Learning Representations (ICLR), 2018.
, Non-local neural networks. CoRR, 2017.
, A-fast-rcnn: Hard positive generation via adversary for object detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.3039-3048, 2017.
DOI : 10.1109/cvpr.2017.324
URL : http://arxiv.org/pdf/1704.03414
, An HOG-LBP human detector with partial occlusion handling, IEEE 12th International Conference on Computer Vision, pp.32-39, 2009.
DOI : 10.1109/iccv.2009.5459207
, Repulsion Loss: Detecting Pedestrians in a Crowd, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Learning steerable filters for rotation equivariant cnns, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, DETRAC: A new benchmark and protocol for multi-object tracking, 2015.
, Iarpa janus benchmark-b face dataset, CVPR Workshop on Biometrics, 2017.
DOI : 10.1109/cvprw.2017.87
, Slidingwindows for rapid object class localization: A parallel technique, Joint Pattern Recognition Symposium, pp.71-81, 2008.
DOI : 10.1007/978-3-540-69321-5_8
, Multi-cue onboard pedestrian detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.794-801, 2009.
DOI : 10.1109/cvpr.2009.5206638
, Stairnet: Top-down semantic aggregation for accurate one shot detection, 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018, pp.1093-1102, 2018.
DOI : 10.1109/wacv.2018.00125
URL : http://arxiv.org/pdf/1709.05788
, Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, pp.446-454, 2017.
DOI : 10.1109/cvprw.2017.60
URL : http://arxiv.org/pdf/1612.01051
, Cluster boosted tree classifier for multi-view, multipose object detection, IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/iccv.2007.4409006
URL : http://iris.usc.edu/Outlines/papers/2007/wu-nev-iccv07.pdf
, Detection of multiple, partially occluded humans in a single image by bayesian combination of edgelet part detectors, 10th IEEE International Conference on Computer Vision (ICCV 2005, pp.90-97, 2005.
, Learning and-or model to represent context and occlusion for car detection and viewpoint estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, pp.1829-1843, 2016.
, Facial Landmark Detection: A Literature Survey. International Journal of Computer Vision (IJCV), To appear, 2018.
, DOTA: A large-scale dataset for object detection in aerial images. CoRR, abs/1711.10398, 2017.
, Estimating the aspect layout of object categories, 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012.
, Data-driven 3d voxel patterns for object category recognition, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, pp.1903-1911, 2015.
, Complexity-adaptive distance metric for object proposals generation, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
, Aggregated residual transformations for deep neural networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition, vol.2017, pp.5987-5995, 2017.
, Deep regionlets for object detection. CoRR, abs/1712.02408, 2017.
, Domain adaptation of deformable part-based models, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.12, pp.2367-2380, 2014.
, Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery. Remote Sensing, vol.9, pp.1312-1331, 2017.
, Face detection by structural models, Image and Vision Computing, vol.32, issue.10, pp.790-799, 2014.
, Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp.2129-2137, 2016.
, From facial parts responses to face detection: A deep learning approach, 2015 IEEE International Conference on Computer Vision, ICCV 2015, pp.3676-3684, 2015.
, Wider face: A face detection benchmark, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.5525-5533, 2016.
, A multi-scale cascade fully convolutional network face detector, 23rd International Conference on Pattern Recognition, pp.633-638, 2016.
, Scene text detection via holistic, multi-channel prediction, 2016.
, Learning multi-frame visual representation for joint detection and tracking of small objects, 2017.
, Imagenet training in minutes, Proceedings of the 47th International Conference on Parallel Processing, vol.1, pp.1-1, 2018.
, Multi-scale context aggregation by dilated convolutions, 2015.
, Dilated residual networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.636-644, 2017.
DOI : 10.1109/cvpr.2017.75
URL : http://arxiv.org/pdf/1705.09914
, BDD100K: A diverse driving video database with scalable annotation tooling, 2018.
, Unitbox: An advanced object detection network, Proceedings of the 2016 ACM Conference on Multimedia Conference, MM 2016, pp.516-520, 2016.
, The Role of Context Selection in Object Detection, Proceedings of the British Machine Vision Conference, 2016.
, Temporal dynamic graph lstm for action-driven video object detection, IEEE International Conference on Computer Vision, 2017.
DOI : 10.1109/iccv.2017.200
URL : http://arxiv.org/pdf/1708.00666
, Wildest faces: Face detection and recognition in violent settings, 2018.
, Wide residual networks, Proceedings of the British Machine Vision Conference, 2016.
DOI : 10.5244/c.30.87
URL : https://hal.archives-ouvertes.fr/hal-01832503
, A multipath network for object detection
DOI : 10.5244/c.30.15
URL : http://www.bmva.org/bmvc/2016/papers/paper015/abstract015.pdf
, Proceedings of the British Machine Vision Conference, 2016.
, ADADELTA: an adaptive learning rate method. CoRR, abs/1212, vol.5701, 2012.
, Visualizing and understanding convolutional networks, Computer Vision-ECCV 2014-13th European Conference, pp.818-833, 2014.
, Visualizing and understanding convolutional networks, Computer Vision-ECCV 2014-13th European Conference, pp.818-833, 2014.
, Gated Bi-directional CNN for Object Detection, Computer Vision-ECCV 2016-14th European Conference, 2016.
DOI : 10.1007/978-3-319-46478-7_22
, Crafting gbd-net for object detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
, Feature selective networks for object detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, A survey of recent advances in face detection, 2010.
, Lq-nets: Learned quantization for highly accurate and compact deep neural networks
, , 2018.
, Is faster R-CNN doing well for pedestrian detection?, Computer Vision-ECCV 2016-14th European Conference, vol.9906, pp.443-457, 2016.
, Citypersons: A diverse dataset for pedestrian detection, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp.4457-4465, 2017.
, Occluded Pedestrian Detection Through Guided Attention in CNNs, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, p.9, 2018.
, S$ ? 3$FD: Single Shot Scale-invariant Face Detector, IEEE International Conference on Computer Vision, 2017.
DOI : 10.1109/iccv.2017.30
URL : http://arxiv.org/pdf/1708.05237
, Occlusionaware R-CNN: detecting pedestrians in a crowd, 2018.
, Singleshot refinement neural network for object detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Shufflenet: An extremely efficient convolutional neural network for mobile devices, 2017.
, Adversarial complementary learning for weakly supervised object localization, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Zigzag learning for weakly supervised object detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, W2f: A weakly-supervised to fully-supervised framework for object detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
DOI : 10.1109/cvpr.2015.7298621
URL : http://arxiv.org/pdf/1504.03293
, Multi-oriented text detection with fully convolutional networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.4159-4167, 2016.
DOI : 10.1109/cvpr.2016.451
URL : http://arxiv.org/pdf/1604.04018
, Single-shot object detection with enriched semantics, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Sign text detection in street view images using an integrated feature. Multimedia Tools and Applications, 2018.
DOI : 10.1007/s11042-018-5975-8
, Pseudo mask augmented object detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, 2018.
, Object detection with deep learning: A review. CoRR, abs/1807.05511, 2018.
, Extend the shallow part of single shot multibox detector via convolutional neural network, 2018.
, Object detectors emerge in deep scene cnns, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
, Learning deep features for scene recognition using places database, Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems, 2014.
DOI : 10.1109/tpami.2017.2723009
, Learning deep features for discriminative localization, 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, pp.2921-2929, 2016.
DOI : 10.1109/cvpr.2016.319
URL : http://arxiv.org/pdf/1512.04150
, ScaleTransferrable Object Detection, Computer Vision and Pattern Recognition (CVPR), 2018 IEEE Conference on, p.10, 2018.
, Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients, 2016.
, East: An efficient and accurate scene text detector, 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017.
DOI : 10.1109/cvpr.2017.283
URL : http://arxiv.org/pdf/1704.03155
, Voxelnet: End-to-end learning for point cloud based 3d object detection. CoRR, abs/1711.06396, 2017.
, Orientation robust object detection in aerial images using deep convolutional neural network, IEEE International Conference on, pp.3735-3739, 2015.
DOI : 10.1109/icip.2015.7351502
, Unpaired imageto-image translation using cycle-consistent adversarial networks, IEEE International Conference on Computer Vision, pp.2242-2251, 2017.
DOI : 10.1109/iccv.2017.244
URL : http://arxiv.org/pdf/1703.10593
, Vision meets drones: A challenge. CoRR, abs/1804.07437, 2018.
, Zeroshot detection, 2018.
, Face detection, pose estimation, and landmark localization in the wild, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.2879-2886, 2012.
, Flow-guided feature aggregation for video object detection, IEEE International Conference on Computer Vision, pp.408-417, 2017.
DOI : 10.1109/iccv.2017.52
URL : http://arxiv.org/pdf/1703.10025
, Deep feature flow for video recognition, 2017 IEEE Conference on Computer Vision and Pattern Recognition, vol.2, p.7, 2017.
DOI : 10.1109/cvpr.2017.441
URL : http://arxiv.org/pdf/1611.07715
, Towards high performance video object detection for mobiles, 2018.
, segDeepM: Exploiting segmentation and context in deep neural networks for object detection, IEEE Conference on Computer Vision and Pattern Recognition, 2015.
, Traffic-sign detection and classification in the wild, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp.2110-2118, 2016.
DOI : 10.1109/cvpr.2016.232
, Edge boxes: Locating object proposals from edges, Computer Vision-ECCV 2014-13th European Conference, 2014.
DOI : 10.1007/978-3-319-10602-1_26
URL : http://research.microsoft.com/en-us/um/people/larryz/ZitnickDollarECCV14edgeBoxes.pdf
, Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks, IEEE Transactions on Image Processing, 2016.
DOI : 10.1109/tip.2016.2548241
URL : http://arxiv.org/pdf/1509.03877