# TVR-Ranking: A Dataset for Ranked Video Moment Retrieval with Imprecise Queries

arXiv: https://arxiv.org/abs/2407.06597 · HTML: https://mlsanity.com/paper/2407.06597/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 9 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [A Dataset for Medical Instructional Video Classification and Question Answering](https://mlsanity.com/paper/2201.12888/) (2022)
- [Finding Moments in Video Collections Using Natural Language](https://mlsanity.com/paper/1907.12763/) (2019)

## Built on by (most cited first, 2 of 2)

- [Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base](https://mlsanity.com/paper/2503.23361/) (2025)
- [Retrieving Any Relevant Moments: Benchmark and Models for Generalized Moment Retrieval](https://mlsanity.com/paper/2605.02623/) (2026)

## Abstract (arXiv)

In this paper, we propose the task of textitRanked Video Moment Retrieval (RVMR) to locate a ranked list of matching moments from a collection of videos, through queries in natural language. Although a few related tasks have been proposed and studied by CV, NLP, and IR communities, RVMR is the task that best reflects the practical setting of moment search. To facilitate research in RVMR, we develop the TVR-Ranking dataset, based on the raw videos and existing moment annotations provided in the TVR dataset. Our key contribution is the manual annotation of relevance levels for 94,442 query-moment pairs. We then develop the NDCG@K, IoU≥ μ evaluation metric for this new task and conduct experiments to evaluate three baseline models. Our experiments show that the new RVMR task brings new challenges to existing models and we believe this new dataset contributes to the research on multi-modality search. The dataset is available at urlhttps://github.com/Ranking-VMR/TVR-Ranking