1 citations · 3 across the 9 of their papers we have counts for
9 papers
Language-Aware Information Maximization for Transductive Few-Shot CLIP
Ghassen Baklouti, Maxime Zanella, Ismail Ben Ayed
Transductive few-shot learning has triggered an abundant literature focusing on vision-only models, but is still at a nascent stage within the recent context of foundational vision…
Vocabulary-free few-shot learning for Vision-Language Models
Maxime Zanella, Clément Fuchs, Ismail Ben Ayed +1
Recent advances in few-shot adaptation for Vision-Language Models (VLMs) have greatly expanded their ability to generalize across tasks using only a few labeled examples. However,…
Online Gaussian Test-Time Adaptation of Vision-Language Models
Clément Fuchs, Maxime Zanella, Christophe De Vleeschouwer
Online test-time adaptation (OTTA) of vision-language models (VLMs) has recently garnered increased attention to take advantage of data observed along a stream to improve future pr…
Realistic Test-Time Adaptation of Vision-Language Models
Maxime Zanella, Clément Fuchs, Christophe De Vleeschouwer +1
The zero-shot capabilities of Vision-Language Models (VLMs) have been widely leveraged to improve predictive performance. However, previous works on transductive or test-time adapt…
Boosting Vision-Language Models for Histopathology Classification: Predict all at once
Maxime Zanella, Fereshteh Shakeri, Yunshi Huang +2
The development of vision-language models (VLMs) for histo-pathology has shown promising new usages and zero-shot performances. However, current approaches, which decompose large s…
Enhancing Remote Sensing Vision-Language Models for Zero-Shot Scene Classification
Karim El Khoury, Maxime Zanella, Benoît Gérin +5
Vision-Language Models for remote sensing have shown promising uses thanks to their extensive pretraining. However, their conventional usage in zero-shot scene classification metho…
Boosting Vision-Language Models with Transduction
Maxime Zanella, Benoît Gérin, Ismail Ben Ayed
Transduction is a powerful paradigm that leverages the structure of unlabeled data to boost predictive accuracy. We present TransCLIP, a novel and computationally efficient transdu…
Low-Rank Few-Shot Adaptation of Vision-Language Models
Maxime Zanella, Ismail Ben Ayed
Recent progress in the few-shot adaptation of Vision-Language Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples withi…
On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?
Maxime Zanella, Ismail Ben Ayed
The development of large vision-language models, notably CLIP, has catalyzed research into effective adaptation techniques, with a particular focus on soft prompt tuning. Conjointl…