5 citations · 5 across the 5 of their papers we have counts for
5 papers
Multi-Cue Anomaly Detection and Localization under Data Contamination
Anindya Sundar Das, Monowar Bhuyan
Visual anomaly detection in real-world industrial settings faces two major limitations. First, most existing methods are trained on purely normal data or on unlabeled datasets assu…
Unmasking Backdoors: An Explainable Defense via Gradient-Attention Anomaly Scoring for Pre-trained Language Models
Anindya Sundar Das, Kangjie Chen, Monowar Bhuyan
Pre-trained language models have achieved remarkable success across a wide range of natural language processing (NLP) tasks, particularly when fine-tuned on large, domain-relevant…
Adaptive Deviation Learning for Visual Anomaly Detection with Data Contamination
Anindya Sundar Das, Guansong Pang, Monowar Bhuyan
Visual anomaly detection targets to detect images that notably differ from normal pattern, and it has found extensive application in identifying defective parts within the manufact…
Few-shot Anomaly Detection in Text with Deviation Learning
Anindya Sundar Das, Aravind Ajay, Sriparna Saha +1
Most current methods for detecting anomalies in text concentrate on constructing models solely relying on unlabeled data. These models operate on the presumption that no labeled an…
Self-Supervised Image-to-Text and Text-to-Image Synthesis
Anindya Sundar Das, Sriparna Saha
A comprehensive understanding of vision and language and their interrelation are crucial to realize the underlying similarities and differences between these modalities and to lear…