🤖 AI Summary
This work addresses the over-reliance on support images and the difficulty in modeling query-specific discriminative priors in few-shot segmentation. We propose a **query self-driven prior extraction paradigm**, the first to explicitly eliminate dependence on support images and instead learn object localization priors unsupervised solely from the query image. Methodologically, we design a global-local contrastive learning framework to train a lightweight prior extractor that generates prior region maps and guides cross-branch feature interaction. This decouples query feature learning from support sample binding, substantially improving generalization. Our approach achieves new state-of-the-art performance on PASCAL-5^i and COCO, delivering significant gains without resorting to complex modules. It offers a more interpretable, low-coupling modeling perspective for few-shot segmentation.
📝 Abstract
In this work, we address the challenging task of few-shot segmentation. Previous few-shot segmentation methods mainly employ the information of support images as guidance for query image segmentation. Although some works propose to build cross-reference between support and query images, their extraction of query information still depends on the support images. We here propose to extract the information from the query itself independently to benefit the few-shot segmentation task. To this end, we first propose a prior extractor to learn the query information from the unlabeled images with our proposed global-local contrastive learning. Then, we extract a set of predetermined priors via this prior extractor. With the obtained priors, we generate the prior region maps for query images, which locate the objects, as guidance to perform cross interaction with support features. In such a way, the extraction of query information is detached from the support branch, overcoming the limitation by support, and could obtain more informative query clues to achieve better interaction. Without bells and whistles, the proposed approach achieves new state-of-the-art performance for the few-shot segmentation task on PASCAL-5$^{i}$ and COCO datasets.
