Papers › Generative Prompt Model for Weakly Supervised Object Localization

Generative Prompt Model for Weakly Supervised Object Localization

19 Jul 2023ICCV 2023 1arXiv:2307.09756archive 2025-07-28

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

DenoisingImage DenoisingLanguage ModelingLanguage ModellingObjectObject LocalizationWeakly-Supervised Object Localizationmodel

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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