# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 4 Datasets for Dense Video Captioning
### Detailed Descriptions  
1. MSVD [49] (Microsoft Video Description) is a pioneering and widely adopted dataset for video captioning tasks. It features short video clips accompanied by descriptive sentences. Each video clip in MSVD is annotated with a single sentence description, providing a concise summary of the clip's content. The MSVD dataset comprises over 1970 video clips, offering a substantial collection for research and development in video captioning. 2. TACoS [50] (Textually Annotated Cooking Scenes) is a multimodal corpus containing high-quality videos of cooking activities aligned with multiple natural language descriptions. This facilitates grounding action descriptions in visual information. The corpus provides sentence-level alignments between textual descriptions and video segments, alongside annotations indicating similarity between action descriptions. TACoS comprises 127 videos with 2540 textual descriptions, resulting in 2206 aligned descriptions after filtering. It contains 146,771 words and 11,796 unique sentences. 3. MPII-MD [51] is a dataset sourced from movies for video description. It aims to facilitate research on generating multi-sentence descriptions for videos. Each video is annotated with multiple temporally localized sentences that cover key events and details. The dataset contains 94 movies, amounting to 68,000 clips with 68.300 multi-sentence descriptions. 4. TGIF [52] (Tumblr GIF) is a large-scale dataset designed for animated GIF description, containing 100,000 GIFs collected from Tumblr and 120,000 natural language descriptions sourced via crowdsourcing. Each GIF is accompanied by multiple sentences to \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**NO** & **Datasets** & **Domain** & **Type** & **Videos** & **Clips** & **Sentences** & **Captions Num** & **Caption Len** & **Year** \\ \hline
1 & MSVD [49] & Open & Scene & 1970 & 1970 & 70,028 & 85,550 & 7.14 & 2011 \\
2 & TACoS [50] & Cooking & Scene & 127 & 7206 & 18,227 & 18,227 & 8.27 & 2013 \\
3 & MPII-MD [51] & Movie & Scene & 94 & 68.337 & 68.375 & 68.375 & 11.05 & 2015 \\
4 & TGIF [52] & Open & Scene & - & 125,781 & 125,781 & 125,781 & 11.28 & 2016 \\
5 & MSR-VT [1] & Open & Scene & 7180 & 10,000 & 200,000 & 927 & 2016 \\
6 & Charades [53] & Daily & Scene & 9484 & - & 27,847 & 27,847 & - & 2016 \\
7 & TVW [54] & Open & Scene & 18,100 & 18,100 & 44.603 & 44.603 & - & 2016 \\
8 & ActivityNet Captions [2] & Open & Instance & 20,000 & 100,000 & 100,000 & 72.976 & 13.48 & 2017 \\
9 & YouCook2 [4] & Cooking & Video & 2000 & 15,400 & 15,400 & 15,400 & 7.7 & 2018 \\
10 & BDDX-X [55] & Traffic & Scene & 6984 & 6984 & 26,228 & 26,228 & 14.5 & 2018 \\
11 & VideoNet [56] & Social Media & Scene & 20,147 & 122,626 & 122,626 & 123,628 & 13.32 & 2018 \\
12 & M-VAD [57] & Movie & Scene & 92 & 48,986 & 55,904 & 46,598 & 12.44 & 2019 \\
13 & VATEX [58] & Open & Scene & 41,250 & 41,250 & 86,000 & 825,000 & 15.25 & 2019 \\
14 & TVC [59] & TV show & Scene & 21,793 & 107,926 & 261,510 & 261,510 & 13.4 & 2020 \\
15 & VIT [21] & Cooking & Video & 8169 & 8169 & - & 5840 & 7.1 & 2020 \\
16 & VC\_NBA 2022 [60] & Basketball & Video & 3977 & 3948 & - & 3977 & - & 2022 \\
17 & WTS [61] & Traffic & Instance & 6061 & 6061 & - & 49,860 & 58.7 & 2023 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of video caption datasets across different domains. capture its visual content. High-quality annotations are ensured through syntactic and semantic validation, ensuring strong visual-textual associations. Workers were selected based on language proficiency and performance. The dataset is split into 90K training, 10K validation, and 10K test GIFs, providing ample data for model training and evaluation. 5. MSR-VTT MSR-VTT [7] (Microsoft Research Video to Text) is a diverse video dataset tailored for various video-to-text generation tasks, including video captioning and dense video captioning. It spans 20 broad categories, offering a rich variety of video content.