Recent advances in image captioning task have led to increasing interests in video captioning task. However, most works on video captioning are focused on generating single input of aggregated features, which hardly deviates from image captioning process and does not fully take advantage of dynamic contents present in videos. We attempt to generate video captions that convey richer contents by temporally segmenting the video with action localization, generating multiple captions from multiple frames, and connecting them with natural language processing techniques, in order to generate a story-like caption. We show that our proposed method can generate captions that are richer in contents and can compete with state-of-the-art method without explicitly using video-level features as input.
In terms of the story-like characteristic of the outcome, @cite_30 are more intimate to our motivation. By aligning scenes with subtitles, and subtitles with passages in the books, they were able to generate story-like description of the scenes. This, however, has an obvious drawback that both books and movies should exist, along with subtitles. Alternatively, one may be able to rely on story-like, human-written ground truth captions for images or videos, but such data are not yet sufficiently available in the research community, and are expensive to collect. Since our work treats the frames from each segment independently, we do not need to rely on additional textual data to generate a story-like caption.
@cite_30
2949433733
Books are a rich source of both fine-grained information, how a character, an object or a scene looks like, as well as high-level semantics, what someone is thinking, feeling and how these states evolve through a story. This paper aims to align books to their movie releases in order to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in current datasets. To align movies and books we exploit a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book. We propose a context-aware CNN to combine information from multiple sources. We demonstrate good quantitative performance for movie book alignment and show several qualitative examples that showcase the diversity of tasks our model can be used for.