Summary of Tvr-ranking: a Dataset For Ranked Video Moment Retrieval with Imprecise Queries, by Renjie Liang et al.
TVR-Ranking: A Dataset for Ranked Video Moment Retrieval with Imprecise Queries
by Renjie Liang, Li Li, Chongzhi Zhang, Jing Wang, Xizhou Zhu, Aixin Sun
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed task of Ranked Video Moment Retrieval (RVMR) aims to retrieve a ranked list of matching moments from a video collection based on natural language queries. The RVMR task is unique in reflecting the practical setting of moment search, and existing datasets lack annotations for relevance levels. To facilitate research, the TVR-Ranking dataset was developed, featuring 94,442 query-moment pairs with manual annotations. The paper proposes a new evaluation metric, NDCG@K, IoU≥μ, to assess model performance. Baseline models were evaluated, demonstrating that RVMR poses new challenges and the proposed dataset contributes to multi-modality search research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about finding specific moments in videos by searching for keywords. Imagine you’re looking for a funny moment from your favorite TV show or a dramatic scene from a movie. The goal is to find all relevant moments and rank them in order of relevance. The researchers created a new dataset with 94,000+ query-moment pairs, where each pair has been labeled as more or less relevant. They also developed a new way to measure how well algorithms perform on this task. The study shows that finding moments in videos is a challenging problem and the proposed dataset can help improve search results. |
