Object detection via a multi-region & semantic segmentation-aware CNN model

Spyros Gidaris 1, 2, 3 Nikos Komodakis 4, 1, 2, 3, 5
3 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.
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Spyros Gidaris, Nikos Komodakis. Object detection via a multi-region & semantic segmentation-aware CNN model. ICCV 2015, IEEE Computer Society, Dec 2015, Santiago, Chile. ⟨10.1109/ICCV.2015.135⟩. ⟨hal-01245664⟩

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