Papers › Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization
Adaptively Denoising Proposal Collection forWeakly Supervised Object Localization
Wenju Xu, Yuanwei Wu, Wenchi Ma, Guanghui Wang
In this paper, we address the problem of weakly supervisedobject localization (WSL), which trains a detection network on the datasetwith only image-level annotations. The proposed approach is built on theobservation that the proposal set from the training dataset is a collectionof background, object parts, and objects. Several strategies are taken toadaptively eliminate the noisy proposals and generate pseudo object-levelannotations for the weakly labeled dataset. A multiple instance learning(MIL) algorithm enhanced by mask-out strategy is adopted to collect theclass-specific object proposals, which are then utilized to adapt a pre-trained classification network to a detection network. In addition, thedetection results from the detection network are re-weighted by jointlyconsidering the detection scores and the overlap ratio of proposals in aproposal subset optimization framework. The optimal proposals work asobject-level labels that enable a pseudo-strongly supervised dataset fortraining the detection network. Consequently, we establish a fully adap-tive detection network. Extensive evaluations on the PASCAL VOC 2007and 2012 datasets demonstrate a significant improvement compared withthe state-of-the-art methods.
Code
No code repository is listed for this paper in the archive or in Syntology's graph.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Weakly Supervised Object Detection | PASCAL VOC 2007 | Our scheme | MAP | 40.9 | #35 of 41 | Archive leaderboard | report |
| Weakly Supervised Object Detection | PASCAL VOC 2012 test | Our scheme | MAP | 35.2 | #32 of 32 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections