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Divide and Conquer: Question-Guided Spatio-Temporal Contextual Attention for Video Question Answering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| QueST vs HME | Video QA · TGIF-QA FrameQA | accuracy: 53.8 -> 59.7 | Helped |
| QueST vs HME | Video QA · TGIF-QA Count | MSE: 4.02 -> 4.19 | Hurt |
| QueST vs HME | Video QA · MSRVTT-QA | accuracy: 33.0 -> 34.6 | Helped |
| QueST vs HME | Video QA · MSVD-QA | accuracy: 33.7 -> 36.1 | Helped |
| add video-guided word attention (ablation) | QueST · TGIF-QA FrameQA | accuracy: 58.5 -> 59.7 | Helped |
| add diversity loss (ablation) | QueST · TGIF-QA Transition | accuracy: 79.3 -> 81.0 | Helped |
| add diversity loss (ablation) | QueST · TGIF-QA Count | MSE: 4.17 -> 4.19 | No clear effect |
| replace ST-VQA attention with contextual attention blocks (ablation) | QueST · TGIF-QA FrameQA | accuracy: 56.6 -> 59.0 | Helped |
| multi-scale temporal CAB instead of single-scale (ablation) | QueST · TGIF-QA Action | accuracy: 74.1 -> 75.9 | Helped |
Closest papers by meaning
- TGIF-QA: Toward Spatio-Temporal Reasoning in Visual Question Answering (CVPR 2017 · similarity 0.91)
- Language-aware Visual Semantic Distillation for Video Question Answering (CVPR 2024 · similarity 0.89)
- Tool-Augmented Spatiotemporal Reasoning for Streamlining Video Question Answering Task (NeurIPS 2025 · similarity 0.89)
- STAIR: Spatial-Temporal Reasoning with Auditable Intermediate Results for Video Question Answering (AAAI 2024 · similarity 0.87)
- Granularity-Adaptive Spatial Evidence Tokenization for Video Question Answering (AAAI 2025 · similarity 0.87)
- Temporal Chain of Thought: Long-Video Understanding by Thinking in Frames (NeurIPS 2025 · similarity 0.87)
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