Lighthouse: A User-Friendly Library for Reproducible Video Moment Retrieval and Highlight Detection
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Abstract (arXiv)
We propose Lighthouse, a user-friendly library for reproducible video moment retrieval and highlight detection (MR-HD). Although researchers proposed various MR-HD approaches, the research community holds two main issues. The first is a lack of comprehensive and reproducible experiments across various methods, datasets, and video-text features. This is because no unified training and evaluation codebase covers multiple settings. The second is user-unfriendly design. Because previous works use different libraries, researchers set up individual environments. In addition, most works release only the training codes, requiring users to implement the whole inference process of MR-HD. Lighthouse addresses these issues by implementing a unified reproducible codebase that includes six models, three features, and five datasets. In addition, it provides an inference API and web demo to make these methods easily accessible for researchers and developers. Our experiments demonstrate that Lighthouse generally reproduces the reported scores in the reference papers. The code is available at https://github.com/line/lighthouse.
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 18 of its 25 references that are in the map. First posted on arXiv 6 Aug 2024.
Builds on
18 of 25 references.
References · 18
- 2017Attention Is All You Needcited by 60,106
- 2015Deep Residual Learning for Image Recognitioncited by 51,950
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,588
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,321
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,549
- 2020End-to-End Object Detection with Transformerscited by 7,396
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,264
- 2017The Kinetics Human Action Video Datasetcited by 1,900
- 2018SlowFast Networks for Video Recognitioncited by 1,864
- 2017Dense-Captioning Events in Videoscited by 790
- 2017Localizing Moments in Video with Natural Languagecited by 661
- 2020HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-trainingcited by 362
6 more
- 2020Span-based Localizing Network for Natural Language Video Localizationcited by 234
- 2023Query-Dependent Video Representation for Moment Retrieval and Highlight Detectioncited by 166
- 2023Knowing Where to Focus: Event-aware Transformer for Video Groundingcited by 79
- 2024TR-DETR: Task-Reciprocal Transformer for Joint Moment Retrieval and Highlight Detectioncited by 59
- 2023Correlation-Guided Query-Dependency Calibration for Video Temporal Groundingcited by 31
- 2024Chrono: A Simple Blueprint for Representing Time in MLLMscited by 17
Built on by
Citing papers per year: 2024: 1; 2025: 3; 2026: 1.
5 papers.
- 2024Language-based Audio Moment Retrievalcited by 4
- 2025When One Moment Isn't Enough: Multi-Moment Retrieval with Cross-Moment Interactionscited by 3
- 2025CASTELLA: Long Audio Dataset with Captions and Temporal Boundariescited by 3
- 2025LLM-powered Query Expansion for Enhancing Boundary Prediction in Language-driven Action Localizationcited by 0
- 2026Conformal Coverage Guarantees for Any Video Temporal Groundercited by 0
- 2026Conformal Coverage Guarantees for Any Video Temporal Grounder7 Aug 2026
- 2025CASTELLA: Long Audio Dataset with Captions and Temporal Boundaries19 Nov 2025
- 2025When One Moment Isn't Enough: Multi-Moment Retrieval with Cross-Moment Interactions20 Oct 2025
- 2025LLM-powered Query Expansion for Enhancing Boundary Prediction in Language-driven Action Localization30 May 2025
- 2024Language-based Audio Moment Retrieval24 Sep 2024