# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 4 Datasets for Dense Video Captioning
### Detailed Descriptions
M-VAD M-VAD [57] (Montreal Video Annotation Dataset) is a large-scale movie description dataset consisting of video clips from various movies. It aims to support research on video captioning and related tasks. Each video clip is paired with a textual description. Annotations include face tracks associated with characters mentioned in the captions. The dataset contains over 92 movies, resulting in a substantial number of video clips and textual descriptions, providing rich material for video captioning research. 13. VATEX VATEX [58] (Video and Text) is a large-scale dataset comprising 41,269 video clips, each approximately 10 s long, sourced from the Kinetics-600 dataset. Each video clip in VATEX is annotated with 10 English and 10 Chinese descriptions, providing rich linguistic information across two languages. VATEX boasts a total of 41,269 video clips, making it a substantial resource for video description tasks. 14. TVC TVC [59] (TV Show Caption) is a large-scale multimodal video caption dataset built upon the TVR dataset. It requires systems to gather information from both video and subtitles to generate relevant descriptions. Each annotated moment in TVR has additional descriptions collected to form the TVC dataset, totaling 262,000 descriptions for 108 [53] moments. Descriptions may focus on video, subtitles, or both. The dataset includes 174,350 training descriptions, 43,580 validation descriptions, and 21,780 public test descriptions, split across numerous videos and moments. 15. ViTT The ViTT [21] (Video Timeline Tags) dataset comprises 8000 untrimmed instructional videos, tailored for tasks that involve video content analysis and tagging. It aims to address the uniformity issue in YouCook2 videos by sampling videos with cooking/recipe labels from YouTube-8M. Each video in ViTT is annotated with 7.1 temporally-localized short tags, offering detailed insights into various aspects of the video content. Annotators have identified each step and assigned descriptive yet concise tags. The ViTT dataset boasts 8000 videos, with an average duration of 250 s per video. It contains a vast array of unique tags and token types, making it a comprehensive resource for video analysis tasks. 16. VC_NBA_2022 VC_NBA_2022 VC_NBA_2022 [60] is a specialized basketball video dataset designed for knowledge-guided entity-aware video captioning tasks. This dataset is intended to support the generation of text descriptions that include specific entity names and fine-grained actions, particularly tailored for basketball live text broadcasts. VC_NBA_2022 provides annotations beyond conventional video captions. It leverages a multimodal basketball game knowledge graph (KG_NBA_2022) to offer additional context and knowledge, such as player images and names. Each video segment is annotated with detailed captions covering 9 types of fine-grained shooting events and incorporating player-related information from the knowledge graph. The dataset contains 3977 basketball game videos, with 3162 clips used for training and 786 clips for testing. Each video has one text description and associated candidate player information (images and names). The dataset is constructed to focus on shot and rebound events, which are the most prevalent in basketball games. 1. WTS WTS [61] (Woven Traffic Safety Dataset) is a specialized video dataset designed for fine-grained spatial-temporal understanding tasks in traffic scenarios, particularly focusing on pedestrian-centric events. Comprehensive textual descriptions and unique 3D Gaze data are provided for each video event, capturing detailed behaviors of both vehicles and pedestrians across diverse traffic situations. The dataset contains over 1.2k video events, with rich annotations that enable in-depth analysis and understanding of traffic dynamics.