66 citations · 75 across the 9 of their papers we have counts for
9 papers
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…
Are foundation models for computer vision good conformal predictors?
Leo Fillioux, Julio Silva-Rodríguez, Ismail Ben Ayed +4
Recent advances in self-supervision and contrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progres…
Few-shot Adaptation of Medical Vision-Language Models
Fereshteh Shakeri, Yunshi Huang, Julio Silva-Rodríguez +4
Integrating image and text data through multi-modal learning has emerged as a new approach in medical imaging research, following its successful deployment in computer vision. Whil…
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…
Realistic Evaluation of Transductive Few-Shot Learning
Olivier Veilleux, Malik Boudiaf, Pablo Piantanida +1
Transductive inference is widely used in few-shot learning, as it leverages the statistics of the unlabeled query set of a few-shot task, typically yielding substantially better pe…
Discretely-constrained deep network for weakly supervised segmentation
Jizong Peng, Hoel Kervadec, Jose Dolz +3
An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions. Recent efforts have focused on incorporating such cons…