{"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/transductive-zero-shot-and-few-shot-clip","title":"Transductive Zero-Shot and Few-Shot CLIP","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Ségolène Martin","Yunshi Huang","Fereshteh Shakeri","Jean-Christophe Pesquet","Ismail Ben Ayed"],"abstract":"    Transductive inference has been widely investigated in few-shot image classification but completely overlooked in the recent fast growing literature on adapting vision-langage models like CLIP. This paper addresses the transductive zero-shot and few-shot CLIP classification challenge in which inference is performed jointly across a mini-batch of unlabeled query samples rather than treating each instance independently. This paper addresses the transductive zero-shot and few-shot CLIP classification challenge in which inference is performed jointly across a mini-batch of unlabeled query samples rather than treating each instance independently. We initially construct informative vision-text probability features leading to a classification problem on the unit simplex set. Inspired by Expectation-Maximization (EM) our optimization-based classifying objective models the data probability distribution for each class using a Dirichlet law. The minimization problem is then tackled with a novel block Majorization-Minimization algorithm which simultaneously estimates the distribution parameters and class assignments. Extensivenumerical experiments on 11 datasets underscore the benefits and efficacy of our batch inference approach. On zero-shot tasks with test batches of 75 samples our approach yields near 20% improvement in ImageNet accuracy over CLIP's zero-shot performance. Additionally we outperform state-of-the-art methods in the few-shot setting. Code is available at https://github.com/SegoleneMartin/transductive-CLIP.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Martin_Transductive_Zero-Shot_and_Few-Shot_CLIP_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Martin_Transductive_Zero-Shot_and_Few-Shot_CLIP_CVPR_2024_paper.pdf","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":"transductive-zero-shot-and-few-shot-clip","repo_url":"https://github.com/segolenemartin/transductive-clip","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"few-shot-image-classification","task_name":"Few-Shot Image Classification"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"clip","method_name":"CLIP"},{"method_slug":"transductive-inference","method_name":"Transductive Inference"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}