VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues
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| Format: | Preprint |
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2025
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| author | Swetha, Sirnam Gupta, Rohit Kulkarni, Parth Parag Shatwell, David G Santiago, Jeffrey A Chan Siddiqui, Nyle Fioresi, Joseph Shah, Mubarak |
| author_facet | Swetha, Sirnam Gupta, Rohit Kulkarni, Parth Parag Shatwell, David G Santiago, Jeffrey A Chan Siddiqui, Nyle Fioresi, Joseph Shah, Mubarak |
| contents | 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. Current benchmarks fail to capture this essential aspect of video understanding. To address this gap, we introduce VRR-QA, a benchmark for Visual Relational Reasoning Beyond Explicit Cues. We curate our benchmark from creative and cinematic videos such as movies, that deliberately employ storytelling techniques which omit direct depictions of certain events or relations, requiring viewers to infer them. VRR-QA comprises 1K meticulously expert-annotated QA pairs drawn from 1K creative video clips covering 15 genres across 7 decades of content, from both live-action and animated titles. Our extensive evaluations on 14 leading VideoQA models reveals consistent and significant performance degradation, underscoring their reliance on surface-level visual cues and highlighting the difficulty of implicit reasoning. Even the best model substantially underperforms human baselines with only 64% accuracy. Performance variations across models further illustrate the complexity and diversity of the challenges presented by VRR-QA. By releasing both dataset and data collection framework, VRR-QA establishes a rigorous, diverse, and reproducible testbed for advancing VideoQA: https://swetha5.github.io/ImplicitQA/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21742 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues Swetha, Sirnam Gupta, Rohit Kulkarni, Parth Parag Shatwell, David G Santiago, Jeffrey A Chan Siddiqui, Nyle Fioresi, Joseph Shah, Mubarak Computer Vision and Pattern Recognition 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. Current benchmarks fail to capture this essential aspect of video understanding. To address this gap, we introduce VRR-QA, a benchmark for Visual Relational Reasoning Beyond Explicit Cues. We curate our benchmark from creative and cinematic videos such as movies, that deliberately employ storytelling techniques which omit direct depictions of certain events or relations, requiring viewers to infer them. VRR-QA comprises 1K meticulously expert-annotated QA pairs drawn from 1K creative video clips covering 15 genres across 7 decades of content, from both live-action and animated titles. Our extensive evaluations on 14 leading VideoQA models reveals consistent and significant performance degradation, underscoring their reliance on surface-level visual cues and highlighting the difficulty of implicit reasoning. Even the best model substantially underperforms human baselines with only 64% accuracy. Performance variations across models further illustrate the complexity and diversity of the challenges presented by VRR-QA. By releasing both dataset and data collection framework, VRR-QA establishes a rigorous, diverse, and reproducible testbed for advancing VideoQA: https://swetha5.github.io/ImplicitQA/. |
| title | VRR-QA: Visual Relational Reasoning in Videos Beyond Explicit Cues |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.21742 |