Papers › WSOD2: Learning Bottom-up and Top-down Objectness Distillation forWeakly-supervised...
WSOD2: Learning Bottom-up and Top-down Objectness Distillation forWeakly-supervised Object Detection
Zhaoyang Zeng, Bei Liu, Jianlong Fu, Hongyang Chao, Lei Zhang
We study on weakly-supervised object detection (WSOD)which plays a vital role in relieving human involvement fromobject-level annotations. Predominant works integrate re-gion proposal mechanisms with convolutional neural net-works (CNN). Although CNN is proficient in extracting dis-criminative local features, grand challenges still exist tomeasure the likelihood of a bounding box containing a com-plete object (i.e., “objectness”). In this paper, we pro-pose a novelWSODframework withObjectnessDistillation(i.e.,WSOD2) by designing a tailored training mechanismfor weakly-supervised object detection. Multiple regressiontargets are specifically determined by jointly consideringbottom-up (BU) and top-down (TD) objectness from low-level measurement and CNN confidences with an adaptivelinear combination. As bounding box regression can fa-cilitate a region proposal learning to approach its regres-sion target with high objectness during training, deep ob-jectness representation learned from bottom-up evidencescan be gradually distilled into CNN by optimization. Weexplore different adaptive training curves for BU/TD ob-jectness, and show that the proposed WSOD2can achievestate-of-the-art results.
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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 | WSOD2 | MAP | 53.6 | #13 of 41 | Archive leaderboard | report |
| Weakly Supervised Object Detection | PASCAL VOC 2012 test | WSOD2 | MAP | 47.2 | #16 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.
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