{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fewvs-a-vision-semantics-integration","title":"FewVS: A Vision-Semantics Integration Framework for Few-Shot Image Classification","arxiv_id":null,"date":"2024-10-28","proceeding":"Proceedings of the 32nd ACM International Conference on Multimedia 2024 10","authors":["Zhuoling Li","Yong Wang","Kaitong Li"],"abstract":"Some recent methods address few-shot image classification by extracting semantic information from class names and devising mechanisms for aligning vision and semantics to integrate information from both modalities. However, class names provide only limited information, which is insufficient to capture the visual details in images. As a result, such vision-semantics alignment is inherently biased, leading to suboptimal integration outcomes. In this paper, we avoid such biased vision-semantics alignment by introducing CLIP, a natural bridge between vision and semantics, and enforcing unbiased vision-vision alignment as a proxy task. Specifically, we align features encoded from the few-shot encoder and CLIP's vision encoder on the same image. This alignment is accomplished through a linear projection layer, with a training objective formulated using optimal transport-based assignment prediction. Thanks to...","url_abs":"https://dl.acm.org/doi/pdf/10.1145/3664647.3681427","url_pdf":"https://dl.acm.org/doi/pdf/10.1145/3664647.3681427","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fewvs-a-vision-semantics-integration","repo_url":"https://github.com/zhuolingli/FewVS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"clip","method_name":"CLIP"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}