Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books (1506.06724v1)

Abstract: 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.

• The paper presents a CRF-based model that aligns movie scenes with corresponding book paragraphs to generate visual story-like explanations.
• It utilizes textual features like BLEU scores and contextual embeddings combined with CNN-extracted visual data to enhance alignment accuracy.
• Cross-book experiments demonstrate the model’s scalability and real-world potential in automating visual explanations for literary content.

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

This paper investigates the alignment of textual content from books with visual information from their corresponding movie adaptations. The authors present a model designed to align scenes (shots) from movies with paragraphs from books, aiming to create story-like visual explanations. This involves leveraging both visual and textual data to bridge the gap between narrative descriptions and their visual representations.

Methodology

The primary method involves training a Conditional Random Field (CRF) model that aligns movie shots to corresponding book paragraphs. Several representations and features are used to enhance the model’s performance:

• Textual Features: Including BLEU scores, TF-IDF, and contextual embeddings which capture semantic similarities between movie subtitles and text from books.
• Visual Features: Using Convolutional Neural Networks (CNNs) to extract features from movie frames and scenes, capturing essential visual information which is then aligned with textual data.
• Scene Features and Prior Knowledge: Anchoring the alignment process in the context of the movie’s overall plot structure, helping to map specific shots to paragraphs accurately.

The model benefits from a combination of these features, processed through a multi-layer CNN and further refined with CRF, integrating both global and localized textual-visual alignments.

Results and Performance

Quantitative metrics such as Average Precision (AP) and Recall measure the performance of the alignment model. Across various settings, the highest performance is observed for the CRF model, attaining notable results:

• For "The Green Mile", the CRF model achieves an AP of 27.60 and Recall of 78.23.
• Other combinations of features and models (e.g., UNI, SVM) display lower performance, highlighting the effectiveness of the CRF approach in this task.

Notable qualitative examples further illustrate the model’s capacity to align dialog and scene descriptions from movies with text from corresponding paragraphs. Examples reveal dialogs in movies closely following the book text, demonstrating the model’s attention to both visual and textual cues.

Cross-Book Experiments

The cross-book alignment experiments emphasize the model’s robustness by forcing it to match movie shots to paragraphs from non-corresponding books. Using a dataset of 10 and then 200 books, the approach identifies high-scoring matches based on visual and textual similarity. The larger dataset notably improves the relevance of identified paragraphs, indicating scalability and flexibility of the model to work with extensive textual databases.

Implications and Future Directions

The implications of this research are significant in both practical and theoretical realms. Practically, the system can be employed in applications such as automated creation of visual explanations from texts, enhancing accessibility and educational tools. Theoretically, the work contributes to the understanding of multi-modal data alignment, offering insights into the combination of visual and textual information in machine learning models.

Future developments could explore the extension of this model to other forms of media, including TV shows, graphical novels, or interactive media. Moreover, integrating more sophisticated NLP techniques and advanced visual recognition models could potentially improve the accuracy and depth of alignments. Exploring transformer-based architectures might also provide further enhancements by capturing long-range dependencies in both text and visual data.

The presented findings underscore the intricate relationship between visual and textual storytelling, paving the way for more nuanced and intelligent systems capable of bridging the gap between these two modalities. As AI continues to advance, such interdisciplinary methods will likely become increasingly pivotal in creating comprehensive and context-aware technological solutions.