3 accepted papers
Zero-shot semantic segmentation aims to recognize, pixel-wise, unseen categories without annotated masks, typically by leveraging vision-language models such as CLIP. However, the patch representations obtained by the CLIP's vision encoder lack object-centric structure, making it difficult to locali…
The emergence of vision-language foundation models has enabled the integration of textual information into vision-based applications. However, in few-shot classification and segmentation (FS-CS), this potential remains underutilised. Commonly, self-supervised vision models have been employed, partic…
Foundation models like CLIP and ALIGN have transformed few-shot and zero-shot vision applications by fusing visual and textual data, yet the integrative few-shot classification and segmentation (FS-CS) task primarily leverages visual cues, overlooking the potential of textual support. In FS-CS scena…