Diffusion-Assisted Progressive Learning for Weakly Supervised Phrase Localization

Weakly supervised phrase localization (WSPL) aims to localize visual objects mentioned by given phrases, but learning without human-annotated bounding boxes. Previous works struggle in multi-object scenarios, where objects in the background often simultaneously appear with the target objects. To this end, we propose a Diffusion-Assisted PrOgressive learning framework (i.e., DAPO) for WSPL task in this paper. Specifically, we score the difficulty of training samples based on the quantity of objects and the level of semantic alignment. These samples are then incorporated progressively during training, in an order by their difficulty scores. To address the sample imbalance problem, we propose a Generation-Assisted Tuning (GAT) method for the grounding network. First, to enrich the samples from few-object scenarios, we leverage Stable Diffusion (SD) to generate images with phrases. Second, we introduce an attention-driven scheme to guide SD's attention on mentioned objects. Finally, we design a diffusion-guided loss, which helps the grounding network learn the objects' layouts. Extensive experiments show that our DAPO framework outperforms the strong baselines on benchmark datasets. The source code will be publicly available on GitHub after the double-blind phase.