# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 4 Datasets for Dense Video Captioning
### Characteristics and Limitations of Each Dataset  
1. MSVD MSVD is an early and well-established dataset that features short video clips accompanied by concise descriptions. However, its size and diversity are somewhat limited, and the single sentence descriptions may not fully capture all aspects of the video content. 2. TACoS TACoS is a multimodal corpus that offers video-text alignments along with diverse cooking actions and fine-grained sentence-level annotations. Nevertheless, it is restricted to the cooking domain and thus may lack the diversity needed for broader actions. 3. MPII-MD MPII-MD centers on movie descriptions and contains 94 videos paired with 68k sentences, providing multi-sentence annotations aligned with video clips. But this dataset is confined to movie-related content and lacks the diversity of topics typically found in social media videos. 4. TGIF TGIF consists of 100\(K\) animated GIFs coupled with 120\(K\) natural language descriptions, showcasing rich motion information and cohesive visual stories. Yet, due to the nature of animated GIFs, it lacks audio and long-form narratives, which limits its complexity compared to full videos. 5. MSR-VTT MSR-VTT boasts diverse video content spanning various topics, with multiple captions per video segment to offer a comprehensive understanding. However, the annotations may occasionally be redundant or overlapping, and the quality of captions can vary, potentially affecting model training. 6. Charades Charades, comprising 9,848 annotated videos of daily activities involving 267 people, covers 157 action classes and facilitates research in action recognition and video description. Nonetheless, the dataset's focus on home environments restricts diversity in backgrounds and activities, possibly impacting generalization. 7. VTW The VTW dataset holds 18,100 automatically crawled user-generated videos with diverse titles, presenting a challenging benchmark for video title generation. However, the temporal location and extent of highlights in most videos are unknown, posing challenges for highlight-sensitive methods. 8. ActivityNet Captions ActivityNet Captions is a large-scale dataset characterized by diverse video content, fine-grained temporal annotations, and rich textual descriptions. The complexity of the videos might make it difficult for models to accurately capture all details, and some annotations may exhibit varying levels of granularity. 9. YouCook2 YouCook2 zeroes in on cooking activities, making it highly domain-specific with detailed step-by-step annotations. Compared to general-purpose datasets, it has limited diversity in video content, and its annotations are biased towards cooking terminology. 10. BDD-X BDD-X BDD-X contains over 77 h of driving videos with 26k human-annotated descriptions and explanations, covering diverse driving scenarios and conditions. However, it is important to note that the annotations are rationalizations from observers rather than capturing drivers' internal thought processes. 11. VideoStory VideoStory comprises 20\(k\) social media videos paired with 123\(k\) multi-sentence descriptions, spanning a variety of topics and demonstrating temporal consistency. But there's a possibility that its representativeness might be limited as it tends to focus on engaging content when it comes to all social media videos. 12. M-VAD The M-VAD dataset holds 92 movies with detailed descriptions that cover different genres. It zeroes in on captions for movie clips, which makes it quite suitable for video captioning research. Nevertheless, it lacks character visual annotations as well as proper names in the captions, thereby restricting its application in tasks that require naming capabilities. 13. VATEX VATEX shines in its focus on a broad range of video activities, rendering it versatile for various video understanding tasks. It offers detailed and diverse annotations that touch upon multiple aspects of the video content. Yet, despite being extensive, it may not be deeply specialized in one particular domain like some other datasets.