# MH-DETR: Video Moment and Highlight Detection with Cross-modal Transformer

arXiv: https://arxiv.org/abs/2305.00355 · HTML: https://mlsanity.com/paper/2305.00355/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 66 of its 93 references that are in the map.
- First posted on arXiv 29 Apr 2023.

## Builds on (most cited first, 30 of 66)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Deep Networks with Stochastic Depth](https://mlsanity.com/paper/1603.09382/) (2016)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [Localizing Moments in Video with Natural Language](https://mlsanity.com/paper/1708.01641/) (2017)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [TALL: Temporal Activity Localization via Language Query](https://mlsanity.com/paper/1705.02101/) (2017)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding](https://mlsanity.com/paper/1604.01753/) (2016)
- [MetaFormer Is Actually What You Need for Vision](https://mlsanity.com/paper/2111.11418/) (2021)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Multimodal Learning with Transformers: A Survey](https://mlsanity.com/paper/2206.06488/) (2022)
- [Efficient Attention: Attention with Linear Complexities](https://mlsanity.com/paper/1812.01243/) (2018)
- [Pay Attention to MLPs](https://mlsanity.com/paper/2105.08050/) (2021)
- [Learning 2D Temporal Adjacent Networks for Moment Localization with Natural Language](https://mlsanity.com/paper/1912.03590/) (2019)
- [Span-based Localizing Network for Natural Language Video Localization](https://mlsanity.com/paper/2004.13931/) (2020)
- [TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval](https://mlsanity.com/paper/2001.09099/) (2020)

## Built on by (most cited first, 30 of 40)

- [Knowing Where to Focus: Event-aware Transformer for Video Grounding](https://mlsanity.com/paper/2308.06947/) (2023)
- [TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2401.02309/) (2024)
- [R2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding](https://mlsanity.com/paper/2404.00801/) (2024)
- [Correlation-Guided Query-Dependency Calibration for Video Temporal Grounding](https://mlsanity.com/paper/2311.08835/) (2023)
- [BAM-DETR: Boundary-Aligned Moment Detection Transformer for Temporal Sentence Grounding in Videos](https://mlsanity.com/paper/2312.00083/) (2023)
- [Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2404.09263/) (2024)
- [VTG-GPT: Tuning-Free Zero-Shot Video Temporal Grounding with GPT](https://mlsanity.com/paper/2403.02076/) (2024)
- [Unified Static and Dynamic Network: Efficient Temporal Filtering for Video Grounding](https://mlsanity.com/paper/2403.14174/) (2024)
- [DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding](https://mlsanity.com/paper/2312.02549/) (2023)
- [Moment of Untruth: Dealing with Negative Queries in Video Moment Retrieval](https://mlsanity.com/paper/2502.08544/) (2025)
- [MS-DETR: Towards Effective Video Moment Retrieval and Highlight Detection by Joint Motion-Semantic Learning](https://mlsanity.com/paper/2507.12062/) (2025)
- [Towards Automated Movie Trailer Generation](https://mlsanity.com/paper/2404.03477/) (2024)
- [LD-DETR: Loop Decoder DEtection TRansformer for Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2501.10787/) (2025)
- [Let Me Finish My Sentence: Video Temporal Grounding with Holistic Text Understanding](https://mlsanity.com/paper/2410.13598/) (2024)
- [GPTSee: Enhancing Moment Retrieval and Highlight Detection via Description-Based Similarity Features](https://mlsanity.com/paper/2403.01437/) (2024)
- [Localizing Events in Videos with Multimodal Queries](https://mlsanity.com/paper/2406.10079/) (2024)
- [Multi-modal Fusion and Query Refinement Network for Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2501.10692/) (2025)
- [HiFi-Portrait: Zero-shot Identity-preserved Portrait Generation with High-fidelity Multi-face Fusion](https://mlsanity.com/paper/2512.14542/) (2025)
- [VideoLights: Feature Refinement and Cross-Task Alignment Transformer for Joint Video Highlight Detection and Moment Retrieval](https://mlsanity.com/paper/2412.01558/) (2024)
- [The Devil is in the Spurious Correlations: Boosting Moment Retrieval with Dynamic Learning](https://mlsanity.com/paper/2501.07305/) (2025)
- [CFSum: A Transformer-Based Multi-Modal Video Summarization Framework With Coarse-Fine Fusion](https://mlsanity.com/paper/2503.00364/) (2025)
- [SoccerHigh: A Benchmark Dataset for Automatic Soccer Video Summarization](https://mlsanity.com/paper/2509.01439/) (2025)
- [DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2308.15109/) (2023)
- [Generative Video Diffusion for Unseen Novel Semantic Video Moment Retrieval](https://mlsanity.com/paper/2401.13329/) (2024)
- [Show and Guide: Instructional-Plan Grounded Vision and Language Model](https://mlsanity.com/paper/2409.19074/) (2024)
- [Object-Centric Framework for Video Moment Retrieval](https://mlsanity.com/paper/2512.18448/) (2025)
- [CoSTL: Comprehensive Spatial-Temporal Representation Learning for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2606.01149/) (2026)
- [Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection](https://mlsanity.com/paper/2311.16464/) (2023)
- [QD-VMR: Query Debiasing with Contextual Understanding Enhancement for Video Moment Retrieval](https://mlsanity.com/paper/2408.12981/) (2024)
- [D&M: Enriching E-commerce Videos with Sound Effects by Key Moment Detection and SFX Matching](https://mlsanity.com/paper/2408.13226/) (2024)

## Abstract (arXiv)

With the increasing demand for video understanding, video moment and highlight detection (MHD) has emerged as a critical research topic. MHD aims to localize all moments and predict clip-wise saliency scores simultaneously. Despite progress made by existing DETR-based methods, we observe that these methods coarsely fuse features from different modalities, which weakens the temporal intra-modal context and results in insufficient cross-modal interaction. To address this issue, we propose MH-DETR (Moment and Highlight Detection Transformer) tailored for MHD. Specifically, we introduce a simple yet efficient pooling operator within the uni-modal encoder to capture global intra-modal context. Moreover, to obtain temporally aligned cross-modal features, we design a plug-and-play cross-modal interaction module between the encoder and decoder, seamlessly integrating visual and textual features. Comprehensive experiments on QVHighlights, Charades-STA, Activity-Net, and TVSum datasets show that MH-DETR outperforms existing state-of-the-art methods, demonstrating its effectiveness and superiority. Our code is available at https://github.com/YoucanBaby/MH-DETR.