Event-Causal RAG segments videos into events represented as SES graphs, merges them into a causal knowledge graph, and uses bidirectional retrieval to supply relevant event chains to a video foundation model for improved long-video question answering.
Scenerag: Scene-level retrieval- augmented generation for video understanding
2 Pith papers cite this work. Polarity classification is still indexing.
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VideoStir introduces a spatio-temporal graph-based structure and intent-aware retrieval for long-video RAG, achieving competitive performance with SOTA methods via a new IR-600K dataset.
citing papers explorer
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Event-Causal RAG: A Retrieval-Augmented Generation Framework for Long Video Reasoning in Complex Scenarios
Event-Causal RAG segments videos into events represented as SES graphs, merges them into a causal knowledge graph, and uses bidirectional retrieval to supply relevant event chains to a video foundation model for improved long-video question answering.
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VideoStir: Understanding Long Videos via Spatio-Temporally Structured and Intent-Aware RAG
VideoStir introduces a spatio-temporal graph-based structure and intent-aware retrieval for long-video RAG, achieving competitive performance with SOTA methods via a new IR-600K dataset.