3 citations · 4 across the 14 of their papers we have counts for
17 papers
Histopath-C: Towards Realistic Domain Shifts for Histopathology Vision-Language Adaptation
Mehrdad Noori, Gustavo Adolfo Vargas Hakim, David Osowiechi +6
Medical Vision-language models (VLMs) have shown remarkable performances in various medical imaging domains such as histo\-pathology by leveraging pre-trained, contrastive models t…
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…
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…
CTA: Cross-Task Alignment for Better Test Time Training
Samuel Barbeau, Pedram Fekri, David Osowiechi +4
Deep learning models have demonstrated exceptional performance across a wide range of computer vision tasks. However, their performance often degrades significantly when faced with…
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…
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…
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…
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.…
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…
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 (…
TFS-ViT: Token-Level Feature Stylization for Domain Generalization
Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri +4
Standard deep learning models such as convolutional neural networks (CNNs) lack the ability of generalizing to domains which have not been seen during training. This problem is mai…
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow
David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori +3
A major problem of deep neural networks for image classification is their vulnerability to domain changes at test-time. Recent methods have proposed to address this problem with te…