Boosting Vision-Language Models with Transduction

• URL: http://arxiv.org/abs/2406.01837v1
• Date: Mon, 3 Jun 2024 23:09:30 GMT
• Title: Boosting Vision-Language Models with Transduction
• Authors: Maxime Zanella, Benoît Gérin, Ismail Ben Ayed,
• Abstract summary: We present TransCLIP, a novel and computationally efficient transductive approach for vision-language models. TransCLIP is applicable as a plug-and-play module on top of popular inductive zero- and few-shot models.
• Score: 12.281505126587048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transduction is a powerful paradigm that leverages the structure of unlabeled data to boost predictive accuracy. We present TransCLIP, a novel and computationally efficient transductive approach designed for Vision-Language Models (VLMs). TransCLIP is applicable as a plug-and-play module on top of popular inductive zero- and few-shot models, consistently improving their performances. Our new objective function can be viewed as a regularized maximum-likelihood estimation, constrained by a KL divergence penalty that integrates the text-encoder knowledge and guides the transductive learning process. We further derive an iterative Block Majorize-Minimize (BMM) procedure for optimizing our objective, with guaranteed convergence and decoupled sample-assignment updates, yielding computationally efficient transduction for large-scale datasets. We report comprehensive evaluations, comparisons, and ablation studies that demonstrate: (i) Transduction can greatly enhance the generalization capabilities of inductive pretrained zero- and few-shot VLMs; (ii) TransCLIP substantially outperforms standard transductive few-shot learning methods relying solely on vision features, notably due to the KL-based language constraint.

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