Cog-VADU is a training‑free framework that transforms video anomaly detection into a sequential cognitive reasoning task. It uses Chain‑of‑Anomaly Detection Thought Prompting (CoADTP) to create a recurrent reasoning chain across video segments, preserving temporal memory and distinguishing complex anomalies from high‑motion normal activities. A cross‑modal re‑ranking stage aligns textual rationales with visual embeddings to enforce semantic consistency and temporal coherence, yielding competitive zero‑shot performance on multiple VAD benchmarks.
By Mohd Ubaid Wani, Sara Atito, Josef Kittler, Muhammad Awais
The paper introduces an adaptive temporal modeling framework for weakly supervised video anomaly detection that addresses the limitations of rigid Multiple Instance Learning approaches. It presents a Temporal Refinement Module using dynamic positional encoding and a learnable class token to capture long‑range dependencies, and an Event Segmentation Module that identifies event boundaries via temporal discontinuity analysis to produce discriminative event‑level representations. An adaptive similarity‑based fusion strategy replaces fixed top‑k heuristics, dynamically integrating snippet‑level and event‑level anomaly scores into video‑level predictions, and the method outperforms state‑of‑the‑art baselines on two benchmarks.
By Changyi Li, Yu Xiao
arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.
By Shibo Gao, Peipei Yang, Xu-Yao Zhang, Linlin Huang
arXiv:2608. 08219v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios.
By Rui Wang, Yeteng Wu, Xianling Zhang, Mengshi Qi
PARSEE-VAD is a training‑free online video anomaly detection framework that separates semantic evidence acquisition from score‑state evolution. It uses Proposition‑Aware Reasoning to extract structured propositional evidence from the current causal window and selectively activates more specific queries, while Streaming Evidence Escalation maps this evidence into a compact score‑domain event state and propagates only the bounded state to maintain temporal continuity. Experiments on four benchmarks show strong performance with reduced specialist computation and sparse score‑state propagation, supporting a current‑first principle for streaming multimodal inference.
By Ji Wang, Shuangqing Zhang, Guo-Sen Xie, Fang Zhao
arXiv:2609.07941v2 Announce Type: replace
Abstract: In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Understanding (VAU). Existing VAU methods...
By Chia-Hui Chen, Shih-Ying Yeh, Fu-En Yang, Min-Hung Chen, Shang-Hong Lai