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David Osowiechi

9 accepted papers

2025

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

Moslem Yazdanpanah, Ali Bahri, Mehrdad Noori, Sahar Dastani, Gustavo Adolfo Vargas Hakim, David Osowiechi +2
ICCV 2025poster

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 Purging (PG), a novel backpropagation-free approach that removes tokens highly affected by domain shifts before they reach att…

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2025

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

Ali Bahri, Moslem Yazdanpanah, Sahar Dastani, Mehrdad Noori, Gustavo Adolfo Vargas Hakim, David OSOWIECHI +3
ICML 2025poster

Test-Time Training has emerged as a promising solution to address distribution shifts in 3D point cloud classification. However, existing methods often rely on computationally expensive backpropagation during adaptation, limiting their applicability in real-world, time-sensitive scenarios. In this p…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2025

Spectral Informed Mamba for Robust Point Cloud Processing

Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Sahar Dastani, Milad Cheraghalikhani, Gustavo Adolfo Vargas Hakim +4
CVPR 2025poster

State Space Models (SSMs) have shown significant promise in Natural Language Processing (NLP) and, more recently, computer vision. This paper introduces a new methodology leveraging Mamba and Masked Autoencoder (MAE) networks for point cloud data in both supervised and self-supervised learning. We p…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Spectral State Space Model for Rotation-Invariant Visual Representation Learning

Sahar Dastani, Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, David Osowiechi, Gustavo Adolfo Vargas Hakim +5
CVPR 2025poster

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. SSMs are specifically designed to capture spatially proximate relationships of image patches. However, they fail to ide…

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2025

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

Sahar Dastani, Ali Bahri, Gustavo Adolfo Vargas Hakim, Moslem Yazdanpanah, Mehrdad Noori, David OSOWIECHI +4
NeurIPS 2025poster

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. However, their generalization performance degrades significantly under distribution shifts. To address this limitation, we…

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2025

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

Mehrdad Noori, David OSOWIECHI, Gustavo Adolfo Vargas Hakim, Ali Bahri, Moslem Yazdanpanah, Sahar Dastani +3
NeurIPS 2025poster

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 completely overlooked in dense prediction tasks such as Open-Vocabulary Semantic Segmentation (OVSS). In response, we pr…

Cited by 0Source ↗code ↗Scholar ↗
2024

NC-TTT: A Noise Constrastive Approach for Test-Time Training

David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Moslem Yazdanpanah +2
CVPR 2024highlight

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 recently gained popularity by their ability to enhance the robustness of models through the addition of an auxiliary objective…

Cited by 13Source ↗PDF ↗code ↗Scholar ↗
2024

WATT: Weight Average Test Time Adaptation of CLIP

David OSOWIECHI, Mehrdad Noori, Gustavo Adolfo Vargas Hakim, Moslem Yazdanpanah, Ali Bahri, Milad Cheraghalikhani +4
NeurIPS 2024poster

Vision-Language Models (VLMs) such as CLIP have yielded unprecedented performances for zero-shot image classification, yet their generalization capability may still be seriously challenged when confronted to domain shifts. In response, we present Weight Average Test-Time Adaptation (WATT) of CLIP, a…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2023

ClusT3: Information Invariant Test-Time Training

Gustavo A. Vargas Hakim, David Osowiechi, Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Ismail Ben Ayed +1
ICCV 2023poster

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 (TTT) methods have been developed in an attempt to mitigate these vulnerabilities, where a secondary task is solved at tr…

Cited by 16PDF ↗code ↗Scholar ↗