activity
20192022
most citedFRE: A Fast Method For Anomaly Detection And Segmentation

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20221 cited

FRE: A Fast Method For Anomaly Detection And Segmentation

Ibrahima Ndiour, Nilesh Ahuja, Utku Genc +1

This paper presents a fast and principled approach for solving the visual anomaly detection and segmentation problem. In this setup, we have access to only anomaly-free training da…

cs.CV20221 cited

Anomalib: A Deep Learning Library for Anomaly Detection

Samet Akcay, Dick Ameln, Ashwin Vaidya +3

This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides…

cs.LG2021

Energy-Based Anomaly Detection and Localization

Ergin Utku Genc, Nilesh Ahuja, Ibrahima J Ndiour +1

This brief sketches initial progress towards a unified energy-based solution for the semi-supervised visual anomaly detection and localization problem. In this setup, we have acces…

cs.HC2019

Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students

Nese Alyuz, Eda Okur, Utku Genc +3

We propose a multimodal approach for detection of students' behavioral engagement states (i.e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Pe…

cs.CY2019

Detecting Behavioral Engagement of Students in the Wild Based on Contextual and Visual Data

Eda Okur, Nese Alyuz, Sinem Aslan +3

To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utiliz…