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20222026
most citedClusT3: Information Invariant Test-Time Training

3 citations · 4 across the 11 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CV2025

Purge-Gate: Backpropagation-Free Test-Time Adaptation for Point Clouds Classification via Token Purging

Moslem Yazdanpanah, Ali Bahri, Mehrdad Noori +5

Test-time adaptation (TTA) is crucial for mitigating performance degradation caused by distribution shifts in 3D point cloud classification. In this work, we introduce Token Purgin…

cs.CV2025

TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses

Sahar Dastani, Ali Bahri, Gustavo Adolfo Vargas Hakim +7

State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering architecture designed for vision tasks. How…

cs.CV2025

CLIPTTA: Robust Contrastive Vision-Language Test-Time Adaptation

Marc Lafon, Gustavo Adolfo Vargas Hakim, Clément Rambour +2

Vision-language models (VLMs) like CLIP exhibit strong zero-shot capabilities but often fail to generalize under distribution shifts. Test-time adaptation (TTA) allows models to up…

cs.CV2025

SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds

Ali Bahri, Moslem Yazdanpanah, Sahar Dastani +6

Test-Time Training (TTT) has emerged as a promising solution to address distribution shifts in 3D point cloud classification. However, existing methods often rely on computationall…

cs.CV2025

Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation

Mehrdad Noori, David Osowiechi, Gustavo Adolfo Vargas Hakim +6

Recently, test-time adaptation has attracted wide interest in the context of vision-language models for image classification. However, to the best of our knowledge, the problem is…

cs.CV2025

Spectral Informed Mamba for Robust Point Cloud Processing

Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori +7

State space models have shown significant promise in Natural Language Processing (NLP) and, more recently, computer vision. This paper introduces a new methodology leveraging Mamba…

cs.CV2025

Spectral State Space Model for Rotation-Invariant Visual Representation Learning

Sahar Dastani, Ali Bahri, Moslem Yazdanpanah +8

State Space Models (SSMs) have recently emerged as an alternative to Vision Transformers (ViTs) due to their unique ability of modeling global relationships with linear complexity.…