Active Learning Strategies for Weakly-Supervised Object Detection

Huy V. Vo
Oriane Siméoni
Spyros Gidaris
Andrei Bursuc
Patrick Pérez
Jean Ponce


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.


   title = {Active Learning Strategies for Weakly-Supervised Object Detection},
   author = {Vo, Huy V. and Sim{\'e}oni, Oriane and Gidaris, Spyros and Bursuc, Andrei and P{\'e}rez, Patrick and Ponce, Jean},
   journal = {Proceedings of the European Conference on Computer Vision {(ECCV)}},
   month = {October},
   year = {2022}


This work was supported in part by the Inria/NYU collaboration, the Louis Vuitton/ENS chair on artificial intelligence and the French government under management of Agence Nationale de la Recherche as part of the ``Investissements d’avenir'' program, reference ANR19-P3IA-0001 (PRAIRIE 3IA Institute). It was performed using HPC resources from GENCI–IDRIS (Grant 2021-AD011013055). Huy V. Vo was supported in part by a Valeo/Prairie CIFRE PhD Fellowship.