  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.
