✨VADER: Towards Causal Video Anomaly Understanding with Relation-Aware Large Language Models
📝 Summary:
VADER is an LLM framework enhancing video anomaly understanding. It integrates keyframe object relations and visual cues to provide detailed, causally grounded descriptions and robust question answering, advancing explainable anomaly analysis.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07299
• PDF: https://arxiv.org/pdf/2511.07299
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For more data science resources:
✓ https://t.me/DataScienceT
#LLM #VideoAnalytics #AnomalyDetection #Causality #ExplainableAI
📝 Summary:
VADER is an LLM framework enhancing video anomaly understanding. It integrates keyframe object relations and visual cues to provide detailed, causally grounded descriptions and robust question answering, advancing explainable anomaly analysis.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07299
• PDF: https://arxiv.org/pdf/2511.07299
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#LLM #VideoAnalytics #AnomalyDetection #Causality #ExplainableAI