Research map / Video / Video-language understanding
Weakly Supervised Video Moment Retrieval From Text Queries
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use video-level sentence descriptions and Text-Guided Attention to train without temporal boundary labels | Video moment retrieval · Charades-STA | R@5: 58.17 vs 48.43 | Helped |
| Use video-level sentence descriptions and Text-Guided Attention to train without temporal boundary labels | Video moment retrieval · Charades-STA | R@1: 17.04 vs 23.63; 6.93 vs 8.89 | Mixed |
| Use video-level sentence descriptions and Text-Guided Attention to train without temporal boundary labels | Video moment retrieval · DiDeMo test | R@1: 12.19 vs 13.23 | No clear effect |
| Use video-level sentence descriptions and Text-Guided Attention to train without temporal boundary labels | Video moment retrieval · DiDeMo test | R@5: 39.74 vs 46.98 | Hurt |
Closest papers by meaning
- Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval (AAAI 2024 · similarity 0.88)
- Mitigating Semantic Collapse in Partially Relevant Video Retrieval (NeurIPS 2025 · similarity 0.88)
- Generative Video Diffusion for Unseen Novel Semantic Video Moment Retrieval (AAAI 2025 · similarity 0.88)
- Bridging the Semantic Granularity Gap Between Text and Frame Representations for Partially Relevant Video Retrieval (AAAI 2025 · similarity 0.88)
- TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-Alignment (NeurIPS 2024 · similarity 0.88)
- Anchor-Aware Similarity Cohesion in Target Frames Enables Predicting Temporal Moment Boundaries in 2D (CVPR 2025 · similarity 0.88)
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