8 papers
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…
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…
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.…