7 papers
Prototypical Few-Shot Medical Image Semantic Segmentation with Background Fusion
Yuan Dong, Xiaoyu Yu, Wentao Wan +4
Few-shot Semantic Segmentation (FSS) aims to adapt a pre-trained model to new classes with as few as a single labeled training sample per class. The existing prototypical work used…
Source-Free Domain Adaptation with Vision-Language Prior
Song Tang, Yunxiang Bai, Wenxin Su +3
Source-Free Domain Adaptation (SFDA) seeks to adapt a source model, which is pre-trained on a supervised source domain, for a target domain, with only access to unlabeled target tr…
Consistent text-to-image generation via scene de-contextualization
Song Tang, Peihao Gong, Kunyu Li +5
Consistent text-to-image (T2I) generation seeks to produce identity-preserving images of the same subject across diverse scenes, yet it often fails due to a phenomenon called ident…
Unified Source-Free Domain Adaptation
Song Tang, Wenxin Su, Mao Ye +2
In the pursuit of transferring a source model to a target domain without access to the source training data, Source-Free Domain Adaptation (SFDA) has been extensively explored acro…
Source-Free Domain Adaptive Object Detection with Semantics Compensation
Song Tang, Jiuzheng Yang, Mao Ye +3
Strong data augmentation is a fundamental component of state-of-the-art mean teacher-based Source-Free domain adaptive Object Detection (SFOD) methods, enabling consistency-based s…
Proxy Denoising for Source-Free Domain Adaptation
Song Tang, Wenxin Su, Yan Gan +3
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain with no access to the source data. Inspired by the success of large Visi…