3 accepted papers
Co-salient Object Detection (CoSOD) aims to segment salient objects that consistently appear across a group of related images. Despite the notable progress achieved by recent training-based approaches, they still remain constrained by the closed-set datasets and exhibit limited generalization. Howev
Pursuing training-free open-vocabulary semantic segmentation in an efficient and generalizable manner remains challenging due to the deep-seated spatial bias in CLIP. To overcome the limitations of existing solutions, this work moves beyond the CLIP-based paradigm and harnesses the recent spatially-…
Training-free open-vocabulary semantic segmentation has advanced with vision-language models like CLIP, which exhibit strong zero-shot abilities. However, CLIP's attention mechanism often wrongly emphasises specific image tokens, namely outliers, which results in irrelevant over-activation. Existing…