Papers › Weakly Supervised Deep Detection Networks
Weakly Supervised Deep Detection Networks
Hakan Bilen, Andrea Vedaldi
Weakly supervised learning of object detection is an important problem in image understanding that still does not have a satisfactory solution. In this paper, we address this problem by exploiting the power of deep convolutional neural networks pre-trained on large-scale image-level classification tasks. We propose a weakly supervised deep detection architecture that modifies one such network to operate at the level of image regions, performing simultaneously region selection and classification. Trained as an image classifier, the architecture implicitly learns object detectors that are better than alternative weakly supervised detection systems on the PASCAL VOC data. The model, which is a simple and elegant end-to-end architecture, outperforms standard data augmentation and fine-tuning techniques for the task of image-level classification as well.
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Code
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Code Syntology ran Syntology
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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 | COCO test-dev | WSDDN | AP50 | 11.5 | #4 of 4 | Archive leaderboard | report |
| Weakly Supervised Object Detection | Charades | WSDDN | MAP | 0.65 | #6 of 6 | Archive leaderboard | report |
| Weakly Supervised Object Detection | HICO-DET | WSDDN | MAP | 3.27 | #3 of 4 | Archive leaderboard | report |
| Weakly Supervised Object Detection | PASCAL VOC 2007 | WSDDN-Ens | MAP | 39.3 | #36 of 41 | Archive leaderboard | report |
| Weakly Supervised Object Detection | Watercolor2k | WSDDN | MAP | 12.7 | #12 of 12 | 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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