119 citations · 156 across the 24 of their papers we have counts for
4 papers · 1 filter
LatteCLIP: Unsupervised CLIP Fine-Tuning via LMM-Synthetic Texts
Anh-Quan Cao, Maximilian Jaritz, Matthieu Guillaumin +2
Large-scale vision-language pre-trained (VLP) models (e.g., CLIP) are renowned for their versatility, as they can be applied to diverse applications in a zero-shot setup. However,…
Domain Adaptation with a Single Vision-Language Embedding
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc +2
Domain adaptation has been extensively investigated in computer vision but still requires access to target data at the training time, which might be difficult to obtain in real-wor…
CLIP's Visual Embedding Projector is a Few-shot Cornucopia
Mohammad Fahes, Tuan-Hung Vu, Andrei Bursuc +2
We introduce ProLIP, a simple and architecture-agnostic method for adapting contrastively pretrained vision-language models, such as CLIP, to few-shot classification. ProLIP fine-t…
UMBRAE: Unified Multimodal Brain Decoding
Weihao Xia, Raoul de Charette, Cengiz Öztireli +1
We address prevailing challenges of the brain-powered research, departing from the observation that the literature hardly recover accurate spatial information and require subject-s…