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
1566289585 2949433733 1906515132 118129934
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 the current datasets. To align movies and books we propose 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. 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. Film adaptations of novels often visually display in a few shots what is described in many pages of the source novel. In this paper we present a new problem: to align book chapters with video scenes. Such an alignment facilitates finding differences between the adaptation and the original source, and also acts as a basis for deriving rich descriptions from the novel for the video clips. We propose an efficient method to compute an alignment between book chapters and video scenes using matching dialogs and character identities as cues. A major consideration is to allow the alignment to be non-sequential. Our suggested shortest path based approach deals with the non-sequential alignments and can be used to determine whether a video scene was part of the original book. We create a new data set involving two popular novel-to-film adaptations with widely varying properties and compare our method against other text-to-video alignment baselines. Using the alignment, we present a qualitative analysis of describing the video through rich narratives obtained from the novel. This paper presents a method for compiling a large-scale bilingual corpus from a database of movie subtitles. To create the corpus, we propose an algorithm based on Gale and Church’s sentence alignment algorithm(1993). However, our algorithm not only relies on character length information, but also uses subtitle-timing information, which is encoded in the subtitle files. Timing is highly correlated between subtitles in different versions (for the same movie), since subtitles that match should be displayed at the same time. However, the absolute time values can’t be used for alignment, since the timing is usually specified by frame numbers and not by real time, and converting it to real time values is not always possible, hence we use normalized subtitle duration instead. This results in a significant reduction in the alignment error rate.