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Towards Balanced Alignment: Modal-Enhanced Semantic Modeling for Video Moment Retrieval
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Proposes MESM, which enhances video features at the frame-word level via weight-shared cross-attention with masked word reconstruction and enriches query text at the segment-sentence level with generated complementary tokens, to balance cross-modal alignment for video moment retrieval.
Problem
Video moment retrieval suffers from modality imbalance: video carries far richer semantics than a short query, so direct cross-modal alignment at frame-word and segment-sentence levels is sub-optimal.
Approach
Enhance both modalities before alignment: a weight-shared cross-attention trained with masked language modeling filters query-irrelevant frame features (frame-word level), and a masked sentence token generated from context sentences and the ground-truth segment via contrastive learning adds complementary knowledge to the query (segment-sentence level).
Key results
- On Charades-STA with C+SF and C features, MESM reaches 61.24% R1@0.5 and 38.04% R1@0.7, versus 56.89% and 32.50% for QD-DETR.
- On Charades-CG novel-word split, MESM achieves 31.22% R1@0.7 with C+SF and C, up from 26.19% for QD-DETR.
- On TACoS, MESM reports 52.69% R1@0.3 and 39.52% R1@0.5, improving on the prior best listed (MS-DETR at 47.66% and 37.36%).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply MESM with VGG and GloVe features | QD-DETR · Charades-STA | R1@0.7: 32.69 -> 35.99 | Helped |
| apply MESM with C+SF and C features | QD-DETR · Charades-STA | R1@0.7: 32.50 -> 38.04 | Helped |
| apply MESM | VMR methods using C3D and GloVe · TACoS | R1@0.5: 37.36 -> 39.52 | Helped |
| apply MESM | QD-DETR · QVHighlights test | R1@0.7: 44.98 -> 45.20 | Helped |
| apply MESM | VISA · Charades-CG novel-composition | R1@0.7: 22.71 -> 26.00 | Helped |
| apply MESM | VISA · Charades-CG novel-word | R1@0.7: 20.88 -> 33.67 | Helped |
| add FW-MESM to the encoder-loss variant (ablation) | MESM · Charades-STA | R1@0.7: 35.00 -> 36.26 | Helped |
| add SS-MESM to the encoder-loss variant (ablation) | MESM · Charades-STA | R1@0.7: 35.00 -> 37.39 | Helped |
| add encoder loss to the variant with FW-MESM and SS-MESM (ablation) | MESM · Charades-STA | R1@0.7: 33.17 -> 38.04 | Helped |
| replace two extra MA layers with 2×FW-MESM (ablation) | MESM without SS-MESM · Charades-STA | R1@0.7: 33.05 -> 36.26 | Helped |
| remove weight-shared MLM (ablation) | MESM with SS-MESM · Charades-STA | R1@0.7: 38.04 -> 36.37 | Hurt |
| use 2 SS-MESM layers instead of 4 (ablation) | MESM · Charades-STA | R1@0.7: 38.04 -> 35.32 | Hurt |
Surprising findings
- On QVHighlights, where each video has only one sentence, the segment-sentence module has no context sentences and mainly acts as a prompt for learning from various videos, per the authors' analysis.
- Adding extra cross-attention layers without the masked language modeling objective lowers scores, which the authors attribute to overfitting.
Limitations
- On QVHighlights, with only one sentence per video, the segment-sentence enhancement lacks context and provides limited gain by the authors' own account.
- Gains over MS-DETR on TACoS are attributed to multi-scale features, so comparisons are not fully controlled.
- Results depend on feature extractors and re-implementation of baselines under a shared training scheme.
Experimental setup
Datasets: Charades-STA TACoS QVHighlights Charades-CG
Benchmarks: Charades-STA TACoS QVHighlights Charades-CG
Metrics: R1@0.5 R1@0.7 mIoU mAP mAP_avg
Baselines: 2D-TAN CBLN RaNet DCM MMN UMT QD-DETR MomentDETR VDI M-DETR VSLNet SeqPAN
Builds on: QD-DETR DAB-DETR Moment-DETR
Closest papers by meaning
- CDTR: Semantic Alignment for Video Moment Retrieval Using Concept Decomposition Transformer (AAAI 2025 · similarity 0.91)
- Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language (AAAI 2024 · similarity 0.90)
- TOPA: Extending Large Language Models for Video Understanding via Text-Only Pre-Alignment (NeurIPS 2024 · similarity 0.89)
- Generative Video Diffusion for Unseen Novel Semantic Video Moment Retrieval (AAAI 2025 · similarity 0.89)
- Zero-shot Video Moment Retrieval via Off-the-shelf Multimodal Large Language Models (AAAI 2025 · similarity 0.89)
- Video-RAG: Visually-aligned Retrieval-Augmented Long Video Comprehension (NeurIPS 2025 · similarity 0.88)
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