ESOM is a training-free streaming model for open-world video anomaly detection with dynamic definitions that achieves real-time single-GPU efficiency and state-of-the-art results on a new benchmark.
Vad-r1: Towards video anomaly reasoning via perception-to-cognition chain- of-thought
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2representative citing papers
An agentic three-stage video annotation pipeline with an MSTED intermediate and top-down domain adaptation produces CoT training data that lifts Cosmos-Reason2 past Gemini on traffic event reasoning.
citing papers explorer
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ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions
ESOM is a training-free streaming model for open-world video anomaly detection with dynamic definitions that achieves real-time single-GPU efficiency and state-of-the-art results on a new benchmark.
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MAVEN: A Multi-stage Agentic Annotation Pipeline for Video Reasoning Tasks
An agentic three-stage video annotation pipeline with an MSTED intermediate and top-down domain adaptation produces CoT training data that lifts Cosmos-Reason2 past Gemini on traffic event reasoning.