313 citations · 381 across the 13 of their papers we have counts for
5 papers · 1 filter
A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models
Julio Silva-Rodríguez, Sina Hajimiri, Ismail Ben Ayed +1
Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While signific…
Bag of Tricks for Fully Test-Time Adaptation
Saypraseuth Mounsaveng, Florent Chiaroni, Malik Boudiaf +2
Fully Test-Time Adaptation (TTA), which aims at adapting models to data drifts, has recently attracted wide interest. Numerous tricks and techniques have been proposed to ensure ro…
ClusT3: Information Invariant Test-Time Training
Gustavo A. Vargas Hakim, David Osowiechi, Mehrdad Noori +3
Deep Learning models have shown remarkable performance in a broad range of vision tasks. However, they are often vulnerable against domain shifts at test-time. Test-time training (…
Prompting classes: Exploring the Power of Prompt Class Learning in Weakly Supervised Semantic Segmentation
Balamurali Murugesan, Rukhshanda Hussain, Rajarshi Bhattacharya +2
Recently, CLIP-based approaches have exhibited remarkable performance on generalization and few-shot learning tasks, fueled by the power of contrastive language-vision pre-training…
Open-Set Likelihood Maximization for Few-Shot Learning
Malik Boudiaf, Etienne Bennequin, Myriam Tami +4
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously de…