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cs.CV2026

Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models

Furkan Mumcu, Michael J. Jones, Anoop Cherian +1

Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scenes. While this focus has led…

cs.CV2025

Towards Open-Vocabulary Multimodal 3D Object Detection with Attributes

Xinhao Xiang, Kuan-Chuan Peng, Suhas Lohit +2

3D object detection plays a crucial role in autonomous systems, yet existing methods are limited by closed-set assumptions and struggle to recognize novel objects and their attribu…

cs.CV2025

Programmatic Video Prediction Using Large Language Models

Hao Tang, Kevin Ellis, Suhas Lohit +2

The task of estimating the world model describing the dynamics of a real world process assumes immense importance for anticipating and preparing for future outcomes. For applicatio…

cs.CV2025

UWAV: Uncertainty-weighted Weakly-supervised Audio-Visual Video Parsing

Yung-Hsuan Lai, Janek Ebbers, Yu-Chiang Frank Wang +3

Audio-Visual Video Parsing (AVVP) entails the challenging task of localizing both uni-modal events (i.e., those occurring exclusively in either the visual or acoustic modality of a…

cs.CV2024

Multimodal 3D Object Detection on Unseen Domains

Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng +2

LiDAR datasets for autonomous driving exhibit biases in properties such as point cloud density, range, and object dimensions. As a result, object detection networks trained and eva…

cs.CV2024

Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection

Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng +2

Popular representation learning methods encourage feature invariance under transformations applied at the input. However, in 3D perception tasks like object localization and segmen…