3 citations · 4 across the 11 of their papers we have counts for
11 papers · 1 filter
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…