124 citations · 171 across the 18 of their papers we have counts for
6 papers · 2 filters
Test-Time Adaptation in Point Clouds: Leveraging Sampling Variation with Weight Averaging
Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori +7
Test-Time Adaptation (TTA) addresses distribution shifts during testing by adapting a pretrained model without access to source data. In this work, we propose a novel TTA approach…
FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain Generalization
Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri +5
Domain Generalization techniques aim to enhance model robustness by simulating novel data distributions during training, typically through various augmentation or stylization strat…
WATT: Weight Average Test-Time Adaptation of CLIP
David Osowiechi, Mehrdad Noori, Gustavo Adolfo Vargas Hakim +7
Vision-Language Models (VLMs) such as CLIP have yielded unprecedented performance for zero-shot image classification, yet their generalization capability may still be seriously cha…
GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D
Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori +6
We introduce a pioneering approach to self-supervised learning for point clouds, employing a geometrically informed mask selection strategy called GeoMask3D (GM3D) to boost the eff…
CLIPArTT: Adaptation of CLIP to New Domains at Test Time
Gustavo Adolfo Vargas Hakim, David Osowiechi, Mehrdad Noori +5
Pre-trained vision-language models (VLMs), exemplified by CLIP, demonstrate remarkable adaptability across zero-shot classification tasks without additional training. However, thei…
NC-TTT: A Noise Contrastive Approach for Test-Time Training
David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori +5
Despite their exceptional performance in vision tasks, deep learning models often struggle when faced with domain shifts during testing. Test-Time Training (TTT) methods have recen…