7 citations · 11 across the 23 of their papers we have counts for
27 papers
Locality-Attending Vision Transformer
Sina Hajimiri, Farzad Beizaee, Fereshteh Shakeri +3
Vision transformers have demonstrated remarkable success in classification by leveraging global self-attention to capture long-range dependencies. However, this same mechanism can…
OCTOPUS: Enhancing the Spatial-Awareness of Vision SSMs with Multi-Dimensional Scans and Traversal Selection
Kunal Mahatha, Ali Bahri, Pierre Marza +5
State space models (SSMs) have recently emerged as an alternative to transformers due to their unique ability of modeling global relationships in text with linear complexity. Howev…
SPARK: Stochastic Propagation via Affinity-guided Random walK for training-free unsupervised segmentation
Kunal Mahatha, Jose Dolz, Christian Desrosiers
We argue that existing training-free segmentation methods rely on an implicit and limiting assumption, that segmentation is a spectral graph partitioning problem over diffusion-der…
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…
NERVE: Neighbourhood & Entropy-guided Random-walk for training free open-Vocabulary sEgmentation
Kunal Mahatha, Jose Dolz, Christian Desrosiers
Despite recent advances in Open-Vocabulary Semantic Segmentation (OVSS), existing training-free methods face several limitations: use of computationally expensive affinity refineme…
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…
REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport
Farzad Beizaee, Sina Hajimiri, Ismail Ben Ayed +3
Unsupervised anomaly detection (UAD) in brain imaging is crucial for identifying pathologies without the need for labeled data. However, accurately localizing anomalies remains cha…
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…
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…
THUNDER: Tile-level Histopathology image UNDERstanding benchmark
Pierre Marza, Leo Fillioux, Sofiène Boutaj +6
Progress in a research field can be hard to assess, in particular when many concurrent methods are proposed in a short period of time. This is the case in digital pathology, where…
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…
DART: Leveraging Distance for Test Time Adaptation in Person Re-Identification
Rajarshi Bhattacharya, Shakeeb Murtaza, Christian Desrosiers +3
Person re-identification (ReID) models are known to suffer from camera bias, where learned representations cluster according to camera viewpoints rather than identity, leading to s…
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…
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection
Farzad Beizaee, Gregory A. Lodygensky, Christian Desrosiers +1
Recent advances in diffusion models have spurred research into their application for Reconstruction-based unsupervised anomaly detection. However, these methods may struggle with m…
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.…
MAD-AD: Masked Diffusion for Unsupervised Brain Anomaly Detection
Farzad Beizaee, Gregory Lodygensky, Christian Desrosiers +1
Unsupervised anomaly detection in brain images is crucial for identifying injuries and pathologies without access to labels. However, the accurate localization of anomalies in medi…
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…
ReC-TTT: Contrastive Feature Reconstruction for Test-Time Training
Marco Colussi, Sergio Mascetti, Jose Dolz +1
The remarkable progress in deep learning (DL) showcases outstanding results in various computer vision tasks. However, adaptation to real-time variations in data distributions rema…
Harmonizing Flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization
Farzad Beizaee, Gregory A. Lodygensky, Chris L. Adamson +5
Lack of standardization and various intrinsic parameters for magnetic resonance (MR) image acquisition results in heterogeneous images across different sites and devices, which adv…
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…