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Lighthouse: A User-Friendly Library for Reproducible Video Moment Retrieval and Highlight Detection

2024-08-06 · Taichi Nishimura, Shota Nakada, Hokuto Munakata, Tatsuya Komatsu

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.

📄 PDF Abstract BibTeX arXiv:2408.02901

Code (1)

line/lighthouse 공식 구현 pytorch

Tasks

audio moment retrievalHighlight DetectionMoment Retrieval

Methods 이 논문이 사용한 방법론

SET Dynamic Sparse Training method where weight mask is updated randomly periodically
Library 설명 없음

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