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Abstract - CRAFT: Critic-Refined Adaptive Key-Frame Targeting for Multimodal Video Question Answering
Grounded multi-video question answering over real-world news events requires systems to surface query-relevant evidence across heterogeneous video archives while attributing every claim to its supporting source. We introduce CRAFT (Critic-Refined Adaptive Key-Frame Targeting), a query-conditioned pipeline that combines dynamic keyframe selection, per-video ASR with multilingual fallback, and a hybrid critic loop to iteratively verify and repair claims before consolidation. The pipeline integrates UNLI temporal entailment, DeBERTa-v3 cross-claim screening, and a Llama-3.2-3B adjudicator, with a final citation-merging stage that emits each fact once with all supporting source identifiers. On MAGMaR 2026, CRAFT achieves the best overall average (0.739), reference recall (0.810), and citation F1 (0.635). We further evaluate on a MAGMaR-style conversion of WikiVideo with 52 non-overlapping event queries, where CRAFT also performs strongly (0.823 Avg), showing that its claim-centric evidence aggregation generalizes beyond MAGMaR. Ablations show that atomic claims, ASR, and the critic loop drive the main gains over the vanilla query-conditioned baseline. Code and implementation details are publicly available at this https URL.
CRAFT:面向多模态视频问答的批评者优化自适应关键帧定位方法 /
CRAFT: Critic-Refined Adaptive Key-Frame Targeting for Multimodal Video Question Answering
1️⃣ 一句话总结
本文提出了一种名为CRAFT的智能视频问答系统,它能像一位严谨的侦探一样,自动从多个新闻视频中找出与问题最相关的关键画面和语音信息,并通过多次交叉验证来确保每条答案都准确无误地标注了来源,最终在权威测试中取得了领先的准确率和引用可靠性。