3 accepted papers
Transductive Information Maximization (TIM) is a leading transductive few-shot learning method that maximizes the mutual information between query features and their predicted labels, while incorporating supervision from the support set. However, its potential remains underexplored, primarily due to
In a recent strongly emergent literature on few-shot CLIP adaptation Linear Probe (LP) has been often reported as a weak baseline. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. In this work we propose and examine from convex-optimization…
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 infere…