# M-VAD Names: a Dataset for Video Captioning with Naming

arXiv: https://arxiv.org/abs/1903.01489 · HTML: https://mlsanity.com/paper/1903.01489/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding](https://mlsanity.com/paper/2407.15350/) (2024)
- [MICap: A Unified Model for Identity-aware Movie Descriptions](https://mlsanity.com/paper/2405.11483/) (2024)
- [Detecting and Grounding Important Characters in Visual Stories](https://mlsanity.com/paper/2303.17647/) (2023)
- ["Previously on ..." From Recaps to Story Summarization](https://mlsanity.com/paper/2405.11487/) (2024)
- [Integrating Video and Text: A Balanced Approach to Multimodal Summary Generation and Evaluation](https://mlsanity.com/paper/2505.06594/) (2025)

## Abstract (arXiv)

Current movie captioning architectures are not capable of mentioning characters with their proper name, replacing them with a generic "someone" tag. The lack of movie description datasets with characters' visual annotations surely plays a relevant role in this shortage. Recently, we proposed to extend the M-VAD dataset by introducing such information. In this paper, we present an improved version of the dataset, namely M-VAD Names, and its semi-automatic annotation procedure. The resulting dataset contains 63k visual tracks and 34k textual mentions, all associated with character identities. To showcase the features of the dataset and quantify the complexity of the naming task, we investigate multimodal architectures to replace the "someone" tags with proper character names in existing video captions. The evaluation is further extended by testing this application on videos outside of the M-VAD Names dataset.