CRAFT: 基于批评的自适应关键帧目标定位用于多模态视频问答
CRAFT: Critic-Refined Adaptive Key-Frame Targeting for Multimodal Video Question Answering
AI总结：
该研究提出CRAFT方法，通过动态关键帧选择、每视频ASR与多语言回退以及混合批评循环，迭代验证和修复声明，最终实现多模态视频问答的准确证据聚合。
摘要：
基于现实世界新闻事件的多视频问答需要系统在异构视频档案中检索与查询相关的证据，并将每个声明归因于其支持来源。我们介绍了CRAFT（Critic-Refined Adaptive Key-Frame Targeting），一种查询条件的管道，结合动态关键帧选择、每视频ASR与多语言回退以及混合批评循环，以迭代验证和修复声明，然后整合。该管道集成了UNLI时间蕴含、DeBERTa-v3跨声明筛选以及Llama-3.2-3B裁决者，并在最终引用合并阶段发出每个事实一次，附带所有支持来源标识符。在MAGMaR 2026上，CRAFT实现了最佳的总体平均（0.739）、参考召回（0.810）和引用F1（0.635）。我们进一步在WikiVideo的MAGMaR风格转换上进行了评估，包含52个非重叠事件查询，CRAFT也表现出色（0.823 Avg），表明其声明中心的证据聚合能力超越了MAGMaR。消融研究显示，原子声明、ASR和批评循环在超过基本查询条件基线时发挥了主要作用。代码和实现细节可在https://github.com/bhosalems/CRAFT公开获取。
英文摘要：
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 https://github.com/bhosalems/CRAFT.
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