AI RESEARCH
VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues
arXiv CS.CV
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ArXi:2506.21742v3 Announce Type: replace Video Question Answering (VideoQA) has made significant strides by leveraging multimodal learning to align visual and textual modalities. However, current benchmarks overwhelmingly focus on questions answerable through explicit visual content - actions, objects, and events - directly observable within individual frames or short clips. To truly understand videos as humans do, models must go beyond what is directly shown, inferring hidden relationships and contextual cues that are only implied across frames.