Transductive Zero-Shot and Few-Shot CLIP
<|reference_start|>Transductive Zero-Shot and Few-Shot CLIP: 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.<|reference_end|>
@article{martin2024transductive,
    title={Transductive Zero-Shot and Few-Shot CLIP},
    author={S'egol`ene Martin (OPIS, CVN), Yunshi Huang (ETS), Fereshteh Shakeri
  (ETS), Jean-Christophe Pesquet (OPIS, CVN), Ismail Ben Ayed (ETS)},
    journal={arXiv preprint arXiv:2405.18437},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18437},
    primaryClass={cs.CV cs.AI}
}