DeMUL: Decoupled Multimodal Modeling and Unified Localization for Video Moment Retrieval

DeMUL is a novel method for moment retrieval in video corpora, achieving accurate retrieval through decoupled multimodal modeling and unified localization techniques. Its core innovations include decoupled independent encoding and progressive fusion of visual and language modalities, a unified localization framework that jointly handles moment positions and content relevance, and optimized indexing and transfer for video corpora. It has achieved leading performance on multiple benchmark datasets such as ActivityNet, and can be applied to scenarios like video search and intelligent editing.

The Video Moment Retrieval (VMR) task involves locating relevant moment segments in long videos based on natural language queries. It faces three major challenges: semantic gap (large differences between language and visual semantics), temporal complexity (temporal extension of actions and boundary handling), and multimodal fusion (effectively aligning visual and language information). DeMUL proposes a solution of decoupling and unified localization to address these issues.

1. Decoupled Multimodal Modeling: Modality-specific encoders (visual encoder focuses on temporal and spatial aspects, language encoder on syntactic semantics), decoupled representation learning (modality-agnostic semantic representation), progressive fusion (encode first then interact);
2. Unified Localization Mechanism: Multi-scale candidate generation, joint scoring network (semantic matching + boundary precision + temporal coherence), end-to-end training;
3. Video Corpus Expansion: Hierarchical indexing (two levels: video and moment), cross-video semantic transfer.

The network architecture includes a visual encoder (3D CNN/Transformer + temporal attention + multi-scale features), a language encoder (pre-trained LM + hierarchical representation + phrase modeling), cross-modal fusion (attention alignment + bidirectional interaction + gating mechanism), and a localization head (boundary regression + hybrid classification-regression + temporal smoothing). Training strategies: multi-task learning, hard example mining, data augmentation. Inference optimizations: NMS deduplication, multi-scale testing, post-processing calibration.

Supported datasets: ActivityNet Captions, TACoS, Charades-STA, DiDeMo. Evaluation metrics: R@1/IoU=m, R@5/IoU=m, mIoU. Experimental results: Leading baseline performance in all metrics on ActivityNet Captions; ablation experiments verify the effectiveness of decoupled modeling, unified localization, and multi-scale features.

Application scenarios: Video search engines, intelligent video editing, content moderation, educational video analysis, surveillance and security. Comparison: Compared with early VMR methods (e.g., TALL), it extends to corpus scenarios; compared with cross-modal pre-trained models (e.g., CLIP), it adds a targeted localization mechanism; compared with end-to-end detection methods, it enhances the interpretability of semantic matching.

Current limitations: High computational cost, insufficient efficiency in long video processing, need for improved fine-grained understanding, weak cross-domain generalization. Future directions: Efficient inference (distillation/early exit), multimodal expansion (audio/subtitles), interactive retrieval, zero-shot/few-shot learning, causal reasoning.

Project structure: model/ (architecture), data_loader/ (data processing), scripts/ (training and evaluation), etc. Usage process: Prepare dataset → Configure parameters → Train → Evaluate → Inference. Summary: DeMUL provides a new solution of decoupling and unified localization, which is of reference value for research and applications, and video retrieval technology will become increasingly important.