2 citations · 5 across the 10 of their papers we have counts for
6 papers · 1 filter
What Are We Really Measuring? Rethinking Dataset Bias in Web-Scale Natural Image Collections via Unsupervised Semantic Clustering
Amir Hossein Saleknia, Mohammad Sabokrou
In computer vision, a prevailing method for quantifying dataset bias is to train a model to distinguish between datasets. High classification accuracy is then interpreted as eviden…
TIPS Over Tricks: Simple Prompts for Effective Zero-shot Anomaly Detection
Alireza Salehi, Ehsan Karami, Sepehr Noey +4
Anomaly detection identifies departures from expected behavior in safety-critical settings. When target-domain normal data are unavailable, zero-shot anomaly detection (ZSAD) lever…
APML: Adaptive Probabilistic Matching Loss for Robust 3D Point Cloud Reconstruction
Sasan Sharifipour, Constantino Álvarez Casado, Mohammad Sabokrou +1
Training deep learning models for point cloud prediction tasks such as shape completion and generation depends critically on loss functions that measure discrepancies between predi…
Brain-Inspired Stochastic Joint Embedding Representation Learning
Makoto Yamada, Kian Ming A. Chai, Ayoub Rhim +3
Representation learning is one of the key research topics in machine learning, and the framework of self-supervised learning (SSL) has revolutionized computer vision. However, thes…
Enhancing Anomaly Detection Generalization through Knowledge Exposure: The Dual Effects of Augmentation
Mohammad Akhavan Anvari, Rojina Kashefi, Vahid Reza Khazaie +2
Anomaly detection involves identifying instances within a dataset that deviate from the norm and occur infrequently. Current benchmarks tend to favor methods biased towards low div…
Explainability of Vision Transformers: A Comprehensive Review and New Perspectives
Rojina Kashefi, Leili Barekatain, Mohammad Sabokrou +1
Transformers have had a significant impact on natural language processing and have recently demonstrated their potential in computer vision. They have shown promising results over…