Beyond Support Samples: Incorporating Unlabeled Queries for Few-Shot Semantic Segmentation

作者：Yuanwei Liu, Nian Liu, Tao Jiang, Yi Wu, Xiwen Yao, Junwei Han · 发表于：IEEE Transactions on Pattern Analysis and Machine Intelligence · 年份：2026 · DOI：10.1109/tpami.2026.3674742 · 研究领域：Topic Modeling、Natural Language Processing Techniques、Multimodal Machine Learning Applications

Few-shot semantic segmentation (FSS) often struggles with the intra-class diversity issue between query and support images, caused by the category-biased information provided by limited annotated support images for matching objects. While increasing the number of annotated support images could mitigate this bias, it is impractical within the few-shot learning framework. Therefore, our proposed Unlabeled Query Integration Few-Shot Segmentation (UQI-FSS) tackles this challenge by incorporating unlabeled query images into the learning paradigm. This approach aims to achieve a more comprehensive category representation, which is essential to enhance segmentation accuracy in various scenarios. However, integrating unlabeled query images directly requires careful management to prevent the dilution of vital information from the annotated support set. To address this issue, we present an Unlabeled Query Integration Network (UQINet), which adaptively extracts beneficial and suppresses detrimental information from the unlabeled query images. Specifically, we first introduce an Information Bridging Module to close the gap between support and unlabeled query features, generating a pseudo-support set enriched with additional category data. Next, we introduce a Query Fusion Module to incorporate query information from both prototype and pixel levels into the pseudo-support features, thus improving their adaptability to the query. Finally, we propose an Adaptive Selection Module to select e...