Diffusion-Assisted Progressive Learning for Weakly Supervised Phrase Localization
Pengyue Lin, Yanyang Hu, Xinjing Liu, Wenqi Jia, Fangxiang Feng, Ruifan Li
Abstract
Weakly supervised phrase localization (WSPL) aims to localize visual objects mentioned by given phrases, but it learns without human-annotated bounding boxes. Previous works struggle in multi-object scenarios where objects in the background often appear simultaneously 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 used 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 direct SD
BibTeX
@inproceedings{aaai2026_diffusionassiste,
title = {Diffusion-Assisted Progressive Learning for Weakly Supervised Phrase Localization},
author = {Pengyue Lin and Yanyang Hu and Xinjing Liu and Wenqi Jia and Fangxiang Feng and Ruifan Li},
booktitle = {AAAI 2026},
year = {2026}
}