  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.
