Papers › Generative Prompt Model for Weakly Supervised Object Localization
Generative Prompt Model for Weakly Supervised Object Localization
Yuzhong Zhao, Qixiang Ye, Weijia Wu, Chunhua Shen, Fang Wan
Weakly supervised object localization (WSOL) remains challenging when learning object localization models from image category labels. Conventional methods that discriminatively train activation models ignore representative yet less discriminative object parts. In this study, we propose a generative prompt model (GenPromp), defining the first generative pipeline to localize less discriminative object parts by formulating WSOL as a conditional image denoising procedure. During training, GenPromp converts image category labels to learnable prompt embeddings which are fed to a generative model to conditionally recover the input image with noise and learn representative embeddings. During inference, enPromp combines the representative embeddings with discriminative embeddings (queried from an off-the-shelf vision-language model) for both representative and discriminative capacity. The combined embeddings are finally used to generate multi-scale high-quality attention maps, which facilitate localizing full object extent. Experiments on CUB-200-2011 and ILSVRC show that GenPromp respectively outperforms the best discriminative models by 5.2% and 5.6% (Top-1 Loc), setting a solid baseline for WSOL with the generative model. Code is available at https://github.com/callsys/GenPromp.
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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 Localization | CUB-200-2011 | Stable diffusion | GT-known localization accuracy | 98.0 | #3 of 3 | Archive leaderboard | report |
| Weakly-Supervised Object Localization | CUB-200-2011 | Stable diffusion | Top-1 Localization Accuracy | 87.0 | #3 of 3 | Archive leaderboard | report |
| Weakly-Supervised Object Localization | CUB-200-2011 | GenPromp | Top-1 Localization Accuracy | 87.0 | #5 of 10 | Archive leaderboard | report |
| Weakly-Supervised Object Localization | ImageNet | Stable diffusion | GT-known localization accuracy | 75.0 | #1 of 6 | Archive leaderboard | report |
| Weakly-Supervised Object Localization | ImageNet | Stable diffusion | Top-1 Localization Accuracy | 65.2 | #1 of 6 | 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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