  Learning from a large corpus of data, pre-trained models have achieved
impressive progress nowadays. As popular generative pre-training, diffusion
models capture both low-level visual knowledge and high-level semantic
relations. In this paper, we propose to exploit such knowledgeable diffusion
models for mainstream discriminative tasks, i.e., unsupervised object
discovery: saliency segmentation and object localization. However, the
challenges exist as there is one structural difference between generative and
discriminative models, which limits the direct use. Besides, the lack of
explicitly labeled data significantly limits performance in unsupervised
settings. To tackle these issues, we introduce DiffusionSeg, one novel
synthesis-exploitation framework containing two-stage strategies. To alleviate
data insufficiency, we synthesize abundant images, and propose a novel
training-free AttentionCut to obtain masks in the first synthesis stage. In the
second exploitation stage, to bridge the structural gap, we use the inversion
technique, to map the given image back to diffusion features. These features
can be directly used by downstream architectures. Extensive experiments and
ablation studies demonstrate the superiority of adapting diffusion for
unsupervised object discovery.
