LiveBot: Generating Live Video Comments Based on Visual and Textual Contexts
Table 2: Comparison of different video-to-text datasets.
['Dataset', 'Task', '#Video', '#Sentence']
We will release more data in the future. Our Bilibili dataset is among the large-scale dataset in terms of videos (2,361) and sentences (895,929). A major limitation for these datasets is limited domains (i.e. cooking and movie) and small size of data (in terms of videos and sentences). Compared with these datasets, our Bilibili dataset is derived from a wide variety of video categories (19 categories), which can benefit the generalization capability of model learning. In addition, the previous datasets are designed for the tasks of description, question answering, paraphrasing, and captioning, where the patterns between the videos and the language are clear and obvious. Our dataset is for the task of commenting, where the patterns and relationship between the videos and the language are latent and difficult to learn. In summary, our BiliBili dataset represents one of the most comprehensive, diverse, and complex datasets for video-to-text learning.