Active Learning Strategies for Weakly-Supervised Object Detection

Huy V Vo (1, 2) , Oriane Siméoni (2) , Spyros Gidaris (2) , Andrei Bursuc (2) , Patrick Pérez (2) , Jean Ponce (1, 3)

1 DI-ENS - Département d'informatique - ENS Paris
2 Valeo.ai
3 CDS - Center for Data Science [NYU]

Object detectors trained with weak annotations are affordable alternatives to fully-supervised counterparts. However, there is still a significant performance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically selected from the training set using "box-in-box" (BiB), a novel active learning strategy designed specifically to address the well-documented failure modes of weakly-supervised detectors. Experiments on the VOC07 and COCO benchmarks show that BiB outperforms other active learning techniques and significantly improves the base weakly-supervised detector's performance with only a few fully-annotated images per class. BiB reaches 97% of the performance of fully-supervised Fast RCNN with only 10% of fully-annotated images on VOC07. On COCO, using on average 10 fully-annotated images per class, or equivalently 1% of the training set, BiB also reduces the performance gap (in AP) between the weakly-supervised detector and the fully-supervised Fast RCNN by over 70%, showing a good trade-off between performance and data efficiency. Our code is publicly available at https://github.com/huyvvo/BiB.

Mots clés: object detection; weakly-supervised; active learning; annotation-efficient

Domaines: Intelligence artificielle [cs.AI]

Huy V Vo, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, et al.. Active Learning Strategies for Weakly-Supervised Object Detection. European Conference on Computer Vision (ECCV), Oct 2022, Tel Aviv, Israel. ⟨hal-03744614⟩