Transductive Zero-Shot and Few-Shot CLIP

• URL: http://arxiv.org/abs/2405.18437v1
• Date: Mon, 8 Apr 2024 12:44:31 GMT
• Title: Transductive Zero-Shot and Few-Shot CLIP
• Authors: Ségolène Martin, Yunshi Huang, Fereshteh Shakeri, Jean-Christophe Pesquet, Ismail Ben Ayed,
• Abstract summary: This paper addresses the transductive zero-shot and few-shot CLIP classification challenge. Inference is performed jointly across a mini-batch of unlabeled query samples, rather than treating each instance independently. Our approach yields near 20% improvement in ImageNet accuracy over CLIP's zero-shot performance.
• Score: 24.592841797020203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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 classification 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. Extensive numerical 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. The code is available at: https://github.com/SegoleneMartin/transductive-CLIP.

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