# MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment

arXiv: https://arxiv.org/abs/1812.00087 · HTML: https://mlsanity.com/paper/1812.00087/

## Key facts

- Cited by 145 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 30 of 145)

- [VTimeLLM: Empower LLM to Grasp Video Moments](https://mlsanity.com/paper/2311.18445/) (2023)
- [Query-Dependent Video Representation for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2303.13874/) (2023)
- [UMT: Unified Multi-modal Transformers for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2203.12745/) (2022)
- [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021)
- [TubeDETR: Spatio-Temporal Video Grounding with Transformers](https://mlsanity.com/paper/2203.16434/) (2022)
- [Interventional Video Grounding with Dual Contrastive Learning](https://mlsanity.com/paper/2106.11013/) (2021)
- [Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding](https://mlsanity.com/paper/2109.04872/) (2021)
- [Boundary Proposal Network for Two-Stage Natural Language Video Localization](https://mlsanity.com/paper/2103.08109/) (2021)
- [HawkEye: Training Video-Text LLMs for Grounding Text in Videos](https://mlsanity.com/paper/2403.10228/) (2024)
- [Deconfounded Video Moment Retrieval with Causal Intervention](https://mlsanity.com/paper/2106.01534/) (2021)
- [Knowing Where to Focus: Event-aware Transformer for Video Grounding](https://mlsanity.com/paper/2308.06947/) (2023)
- [QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries](https://mlsanity.com/paper/2107.09609/) (2021)
- [Compositional Temporal Grounding with Structured Variational Cross-Graph Correspondence Learning](https://mlsanity.com/paper/2203.13049/) (2022)
- [TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2401.02309/) (2024)
- [MomentDiff: Generative Video Moment Retrieval from Random to Real](https://mlsanity.com/paper/2307.02869/) (2023)
- [Natural Language Video Localization: A Revisit in Span-based Question Answering Framework](https://mlsanity.com/paper/2102.13558/) (2021)
- [On Pursuit of Designing Multi-modal Transformer for Video Grounding](https://mlsanity.com/paper/2109.06085/) (2021)
- [End-to-end Multi-modal Video Temporal Grounding](https://mlsanity.com/paper/2107.05624/) (2021)
- [Memory-Guided Semantic Learning Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2201.00454/) (2022)
- [Towards Visual-Prompt Temporal Answering Grounding in Medical Instructional Video](https://mlsanity.com/paper/2203.06667/) (2022)
- [Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval](https://mlsanity.com/paper/2209.11572/) (2022)
- [A Closer Look at Temporal Sentence Grounding in Videos: Dataset and Metric](https://mlsanity.com/paper/2101.09028/) (2021)
- [MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer](https://mlsanity.com/paper/2305.00355/) (2023)
- [Cross-Sentence Temporal and Semantic Relations in Video Activity Localisation](https://mlsanity.com/paper/2107.11443/) (2021)
- [Weakly Supervised Temporal Adjacent Network for Language Grounding](https://mlsanity.com/paper/2106.16136/) (2021)
- [Temporal Sentence Grounding in Videos: A Survey and Future Directions](https://mlsanity.com/paper/2201.08071/) (2022)
- [SnAG: Scalable and Accurate Video Grounding](https://mlsanity.com/paper/2404.02257/) (2024)
- [Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding](https://mlsanity.com/paper/2203.02966/) (2022)
- [Reducing the Vision and Language Bias for Temporal Sentence Grounding](https://mlsanity.com/paper/2207.13457/) (2022)
- [Zero-shot Natural Language Video Localization](https://mlsanity.com/paper/2110.00428/) (2021)

## Abstract (arXiv)

This research strives for natural language moment retrieval in long, untrimmed video streams. The problem is not trivial especially when a video contains multiple moments of interests and the language describes complex temporal dependencies, which often happens in real scenarios. We identify two crucial challenges: semantic misalignment and structural misalignment. However, existing approaches treat different moments separately and do not explicitly model complex moment-wise temporal relations. In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network. MAN naturally assigns candidate moment representations aligned with language semantics over different temporal locations and scales. Most importantly, we propose to explicitly model moment-wise temporal relations as a structured graph and devise an iterative graph adjustment network to jointly learn the best structure in an end-to-end manner. We evaluate the proposed approach on two challenging public benchmarks DiDeMo and Charades-STA, where our MAN significantly outperforms the state-of-the-art by a large margin.