Cover Image

SRVAU-R1: Enhancing Video Anomaly Understanding via Reflection-Aware Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Zihao, Cao, Shengting, Ye, Muchao
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection
by: Piehl, Mitchell, et al.
Published: (2026)

VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models
by: Ye, Muchao, et al.
Published: (2024)

CueBench: Advancing Unified Understanding of Context-Aware Video Anomalies in Real-World
by: Yu, Yating, et al.
Published: (2025)

SatBLIP: Context Understanding and Feature Identification from Satellite Imagery with Vision-Language Learning
by: Wu, Xue, et al.
Published: (2026)

VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning
by: Zhu, Liyun, et al.
Published: (2025)

ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions
by: Liu, Zihao, et al.
Published: (2026)

Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thought
by: Huang, Chao, et al.
Published: (2025)

Shot-Aware Frame Sampling for Video Understanding
by: Zhao, Mengyu, et al.
Published: (2026)

Hawk: Learning to Understand Open-World Video Anomalies
by: Tang, Jiaqi, et al.
Published: (2024)

EMIT: Enhancing MLLMs for Industrial Anomaly Detection via Difficulty-Aware GRPO
by: Guan, Wei, et al.
Published: (2025)

VideoTG-R1: Boosting Video Temporal Grounding via Curriculum Reinforcement Learning on Reflected Boundary Annotations
by: Dong, Lu, et al.
Published: (2025)

Understanding Real-World Traffic Safety through RoadSafe365 Benchmark
by: Liu, Xinyu, et al.
Published: (2026)

LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding
by: Qiu, Jihao, et al.
Published: (2026)

VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models
by: Cheng, Ying, et al.
Published: (2025)

AD-DINOv3: Enhancing DINOv3 for Zero-Shot Anomaly Detection with Anomaly-Aware Calibration
by: Yuan, Jingyi, et al.
Published: (2025)

Incentivizing Temporal-Awareness in Egocentric Video Understanding Models
by: Xu, Zhiyang, et al.
Published: (2026)

TAU-R1: Visual Language Model for Traffic Anomaly Understanding
by: Lin, Yuqiang, et al.
Published: (2026)

Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection
by: Tao, Chenchen, et al.
Published: (2024)

AI-Generated Video Detection via Spatio-Temporal Anomaly Learning
by: Bai, Jianfa, et al.
Published: (2024)

Text-guided Fine-Grained Video Anomaly Understanding
by: Gu, Jihao, et al.
Published: (2025)

OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning
by: Zhao, Shifang, et al.
Published: (2025)

TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning
by: Wu, Tao, et al.
Published: (2025)

CurveStream: Boosting Streaming Video Understanding in MLLMs via Curvature-Aware Hierarchical Visual Memory Management
by: Wang, Chao, et al.
Published: (2026)

VideoCap-R1: Enhancing MLLMs for Video Captioning via Structured Thinking
by: Meng, Desen, et al.
Published: (2025)

Video Anomaly Detection with Semantics-Aware Information Bottleneck
by: Li, Juntong, et al.
Published: (2025)

HierarQ: Task-Aware Hierarchical Q-Former for Enhanced Video Understanding
by: Azad, Shehreen, et al.
Published: (2025)

Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection
by: Jiang, Yuxin, et al.
Published: (2025)

Language-guided Open-world Video Anomaly Detection under Weak Supervision
by: Liu, Zihao, et al.
Published: (2025)

VQAttack: Transferable Adversarial Attacks on Visual Question Answering via Pre-trained Models
by: Yin, Ziyi, et al.
Published: (2024)

Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection
by: Pu, Yujiang, et al.
Published: (2023)

VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree
by: Li, Wenlong, et al.
Published: (2025)

Enhancing Scene Transition Awareness in Video Generation via Post-Training
by: Shen, Hanwen, et al.
Published: (2025)

Weakly Supervised Video Anomaly Detection with Anomaly-Connected Components and Intention Reasoning
by: Wang, Yu, et al.
Published: (2026)

VideoSeg-R1:Reasoning Video Object Segmentation via Reinforcement Learning
by: Xu, Zishan, et al.
Published: (2025)

VideoMem: Enhancing Ultra-Long Video Understanding via Adaptive Memory Management
by: Jin, Hongbo, et al.
Published: (2025)

VisionCreator-R1: A Reflection-Enhanced Native Visual-Generation Agentic Model
by: Lai, Jinxiang, et al.
Published: (2026)

Rethinking Metrics and Benchmarks of Video Anomaly Detection
by: Liu, Zihao, et al.
Published: (2025)

PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness
by: Pan, Yurui, et al.
Published: (2025)

Kernel-Aware Graph Prompt Learning for Few-Shot Anomaly Detection
by: Tao, Fenfang, et al.
Published: (2024)

VideoAgent2: Enhancing the LLM-Based Agent System for Long-Form Video Understanding by Uncertainty-Aware CoT
by: Zhi, Zhuo, et al.
Published: (2025)