Research map / Video / Video-language understanding
Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes SpotVMR, a light-weight recursive clip-selection module that previews long videos with cheap BAM semantic-index features and only extracts expensive features for query-relevant clips, serving as a plug-and-play speedup for VMR models.
Problem
Video moment retrieval using language (VMR) on long untrimmed videos is computationally infeasible because existing methods extract expensive features for densely sampled fixed-length clips, and fixed-length down-sampling can blur true moment boundaries.
Approach
A RetrievalSpotter recursively selects query-relevant clips using cheap semantic-index features (background, appearance, motion; BAM) fused with query features via cross-modal interaction, so expensive C3D clip features are computed only for selected clips; a teacher-student feature distillation loss and a clip-selection budget loss stabilize joint training.
Key results
- On Charades-STA, SpotVMR reaches R@1 IoU=0.5 of 68.82 versus 57.53 for the best prior baseline ACRM (ACRM listed at 57.53 in Table 1).
- On ActivityNet Captions, SpotVMR achieves R@1 IoU=0.5 of 52.83 versus 48.59 for MMN.
- As a plug-and-play module, it improves MMN on Charades-STA R@1 IoU=0.5 from 47.31 to 49.12 and 2D-TAN on ActivityNet from 44.51 to 46.21, while being more efficient (total time 37.68 s vs 207.82–313.21 s for baselines on 100 ActivityNet videos).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply SpotVMR instead of MMN | MMN · ActivityNet Captions | R@1, IoU=0.5: 48.59 -> 52.83 | Helped |
| apply SpotVMR instead of ACRM | ACRM · Charades-STA | R@1, IoU=0.5: 57.53 -> 68.82 | Helped |
| apply SpotVMR instead of MMN | MMN · TACoS | R@1, IoU=0.3: 39.24 -> 48.72 | Helped |
| add query-aware visual enhancement loss Lqav (ablation) | SpotVMR · ActivityNet Captions | R@1, IoU=0.5: 48.72 -> 52.83 | Helped |
| add clip selection loss Lsel (ablation) | SpotVMR · ActivityNet Captions | R@1, IoU=0.5: 50.66 -> 52.83 | Helped |
| add feature distillation loss Lftd (ablation) | SpotVMR · ActivityNet Captions | R@1, IoU=0.5: 51.39 -> 52.83 | Helped |
| add motion features to background and appearance features (ablation) | SpotVMR semantic index · ActivityNet Captions | R@1, IoU=0.5: 50.24 -> 52.83 | Helped |
| add appearance features to background and motion features (ablation) | SpotVMR semantic index · ActivityNet Captions | R@1, IoU=0.5: 51.35 -> 52.83 | Helped |
| add background features to appearance and motion features (ablation) | SpotVMR semantic index · ActivityNet Captions | R@1, IoU=0.5: 51.98 -> 52.83 | Helped |
| increase recursive steps from 5 to 7 (ablation) | SpotVMR · ActivityNet Captions | R@1, IoU=0.5: 52.83 -> 53.04 | Helped |
| increase recursive steps from 5 to 7 (ablation) | SpotVMR · ActivityNet Captions | R@1, IoU=0.7: 32.76 -> 31.95 | Hurt |
Surprising findings
- Removing the feature distillation loss gives the largest ablation drop on Charades-STA R@1 IoU=0.7 among components, showing optimization of the selector matters.
- The paper's reported best hyperparameters in the text (alpha=0.4, beta=0.8, gamma=0.6) differ from those in the parameter analysis section (beta=0.6, gamma=0.8), an internal inconsistency.
Limitations
- Sensitive to the maximum recursive step count (best at 5; 7 steps lower on R@1 IoU=0.7).
- Relies on several frozen pretrained encoders (EfficientNet-b0, VICReg, C3D) for indexing and features.
- Evaluated only on three short-to-medium-length benchmarks rather than truly hours-long videos.
Experimental setup
Datasets: ActivityNet Captions TACoS Charades-STA
Benchmarks: ActivityNet Captions Charades-STA TACoS
Metrics: R@1 IoU=0.5 R@1 IoU=0.7 R@5 IoU=0.5 R@5 IoU=0.7 R@1 IoU=0.3 queries per second time complexity (s)
Baselines: CTRL SCDM CMIN 2D-TAN DRN MMN GDP LGI VSLNet IVG-DCL ACRM
Builds on: VSLNet MMN 2D-TAN GloVe EfficientNet VICReg C3D
Closest papers by meaning
- Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large Language Models (AAAI 2025 · similarity 0.91)
- Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval (AAAI 2024 · similarity 0.90)
- Generative Video Diffusion for Unseen Novel Semantic Video Moment Retrieval (AAAI 2025 · similarity 0.90)
- VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding (NeurIPS 2024 · similarity 0.89)
- When One Moment Isn't Enough: Multi-Moment Retrieval with Cross-Moment Interactions (NeurIPS 2025 · similarity 0.89)
- MomentSeeker: A Task-Oriented Benchmark For Long-Video Moment Retrieval (NeurIPS 2025 · similarity 0.89)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.