  Recent advances in denoising diffusion probabilistic models have shown great
success in image synthesis tasks. While there are already works exploring the
potential of this powerful tool in image semantic segmentation, its application
in weakly supervised semantic segmentation (WSSS) remains relatively
under-explored. Observing that conditional diffusion models (CDM) is capable of
generating images subject to specific distributions, in this work, we utilize
category-aware semantic information underlied in CDM to get the prediction mask
of the target object with only image-level annotations. More specifically, we
locate the desired class by approximating the derivative of the output of CDM
w.r.t the input condition. Our method is different from previous diffusion
model methods with guidance from an external classifier, which accumulates
noises in the background during the reconstruction process. Our method
outperforms state-of-the-art CAM and diffusion model methods on two public
medical image segmentation datasets, which demonstrates that CDM is a promising
tool in WSSS. Also, experiment shows our method is more time-efficient than
existing diffusion model methods, making it practical for wider applications.
