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EC-RAG: Event Chain Retrieval-Augmented Generation for Long Video Understanding

Paper recorded by Signals 4 on 2026-10-06 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.CV · 计算机视觉 · first seen 2026-10-07

Abstract

Current large video-language models (LVLMs) still face challenges when dealing with long videos, mainly because frames are often processed independently, making it difficult to capture temporal dependencies across events. Although retrieval-augmented approaches have been introduced to provide additional context, most of them operate at the frame or snippet level, which limits their ability to mode

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#345 most recent of 756 cs.CV papers we have recorded · ↑ newer: RenderBench: Benchmarking Render-to-Real Video Transfer with Reconstru · ↓ older: PDB: Point-Based Deformation Blending for Facial Animation Retargeting
Cite this page: EC-RAG: Event Chain Retrieval-Augmented Generation for Long Video Understanding: the #345 most recent of 756 cs.CV papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/ec-rag-event-chain-retrieval-augmented-generation-for-long-video-understanding.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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