Computer Science / Computer Vision

FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation

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๐Ÿ“ Original Info

  • Title: FastV-RAG: Towards Fast and Fine-Grained Video QA with Retrieval-Augmented Generation
  • ArXiv ID: 2601.01513
  • Date: 2026-01-04
  • Authors: Gen Li, Peiyu Liu

๐Ÿ“ Abstract

Vision-Language Models (VLMs) excel at visual reasoning but still struggle with integrating external knowledge. Retrieval-Augmented Generation (RAG) is a promising solution, but current methods remain inefficient and often fail to maintain high answer quality. To address these challenges, we propose VideoSpecu-lateRAG, an efficient VLM-based RAG framework built on two key ideas. First, we introduce a speculative decoding pipeline: a lightweight draft model quickly generates multiple answer candidates, which are then verified and refined by a more accurate heavyweight model, substantially reducing inference latency without sacrificing correctness. Second, we identify a major source of error-incorrect entity recognition in retrieved knowledge-and mitigate it with a simple yet effective similarity-based filtering strategy that improves entity alignment and boosts overall answer accuracy. Experiments demonstrate that VideoSpeculateRAG achieves comparable or higher accuracy than standard RAG approaches while accelerating inference by approximately 2ร—. Our framework highlights the potential of combining speculative decoding with retrieval-augmented reasoning to enhance efficiency and reliability in complex, knowledge-intensive multimodal tasks. The codes are available at https:// github.com/FastVRAG/Fast-VRAG.

๐Ÿ“„ Full Content

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