ACCIDENT is a new benchmark with 2,027 real and 2,211 synthetic annotated video clips for temporal localization, spatial localization, and collision type classification of vehicle accidents in CCTV footage.
Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2representative citing papers
AgentIAD introduces an agentic VLM with Perceptive Zoomer, Web Searcher, and Comparative Retriever tools plus two-stage SFT-then-RL training, achieving 5.92% higher classification accuracy than prior SOTA on the MMAD benchmark.
citing papers explorer
-
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos
ACCIDENT is a new benchmark with 2,027 real and 2,211 synthetic annotated video clips for temporal localization, spatial localization, and collision type classification of vehicle accidents in CCTV footage.
-
AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation
AgentIAD introduces an agentic VLM with Perceptive Zoomer, Web Searcher, and Comparative Retriever tools plus two-stage SFT-then-RL training, achieving 5.92% higher classification accuracy than prior SOTA on the MMAD benchmark.