MovieNet: A Holistic Dataset for Movie Understanding (2007.10937v1)

Abstract: Recent years have seen remarkable advances in visual understanding. However, how to understand a story-based long video with artistic styles, e.g. movie, remains challenging. In this paper, we introduce MovieNet -- a holistic dataset for movie understanding. MovieNet contains 1,100 movies with a large amount of multi-modal data, e.g. trailers, photos, plot descriptions, etc. Besides, different aspects of manual annotations are provided in MovieNet, including 1.1M characters with bounding boxes and identities, 42K scene boundaries, 2.5K aligned description sentences, 65K tags of place and action, and 92K tags of cinematic style. To the best of our knowledge, MovieNet is the largest dataset with richest annotations for comprehensive movie understanding. Based on MovieNet, we set up several benchmarks for movie understanding from different angles. Extensive experiments are executed on these benchmarks to show the immeasurable value of MovieNet and the gap of current approaches towards comprehensive movie understanding. We believe that such a holistic dataset would promote the researches on story-based long video understanding and beyond. MovieNet will be published in compliance with regulations at https://movienet.github.io.

• The paper presents a comprehensive dataset integrating visual and textual data from 1,100 movies for multi-faceted movie analysis.
• The methodology includes extensive annotations for character recognition, scene segmentation, and cinematic style prediction, enabling robust benchmarks.
• The dataset’s holistic design paves the way for future research in narrative understanding, trailer synthesis, and AI-driven film editing.

An Overview of "MovieNet: A Holistic Dataset for Movie Understanding"

The paper "MovieNet: A Holostic Dataset for Movie Understanding" introduces a comprehensive dataset designed to advance research in movie analysis. MovieNet is structured to address the challenges posed by story-based long videos, offering a rich multimedia dataset complemented by extensive annotations. This dataset aims to foster research in areas like story understanding, character recognition, and cinematic style analysis.

Dataset Composition and Structure

MovieNet comprises data from 1,100 movies, encompassing various modalities such as trailers, photos, plot descriptions, and more. There are 1.1 million character annotations with bounding boxes, over 42,000 scene boundaries, and a wide array of tags, including actions, places, and cinematic styles. This makes MovieNet one of the largest and most intricately annotated datasets for movie understanding.

To capture the holistic nature of movies, MovieNet leverages both visual and textual data. Besides the visual elements extracted from the movies themselves, metadata is provided from sources like IMDb and TMDb. This includes genres, cast lists, and directorial information, supplementing the media data for a more enriched analysis framework.

Benchmarks and Experiments

The paper establishes various benchmarks using MovieNet, allowing a multifaceted exploration of movie understanding from genre analysis to intricate cinematic style prediction.

• Genre Analysis: The dataset is leveraged for genre classification tasks, both in image-based and video-based formats. Despite its advantages over previous smaller datasets, MovieNet poses challenges with its long-tailed genre distribution and high semantic level requirements.
• Cinematic Styles: The dataset supports cinematic style prediction with tags for view scale and camera motion across 46,857 shots. Experiments suggest that incorporating subject-based features can significantly enhance prediction accuracy.
• Character Recognition: A significant aspect of MovieNet is its emphasis on characters, providing benchmarks for character detection and identification. The nuanced annotation of character instances helps in training effective models for these tasks.
• Scene Analysis: MovieNet's annotations support scene segmentation and tagging. Here, the dataset's scale provides a robust ground for improving temporal and contextual scene analysis using multiple modalities and semantic cues.
• Story Understanding: MovieNet sets the ground for high-level tasks like movie segment retrieval using synopses, demonstrating the importance of narrative coherence and element tracking in understanding movies.

Implications and Future Directions

One of the substantial contributions of MovieNet is its potential to unify diverse research areas under a common dataset. It extends beyond simple action or scene recognition to encapsulate storytelling, linguistic alignment, and high-level narrative understanding. This holistic approach reflects the need for AI systems to bridge the gap between raw media data and the nuanced understanding of artistic narratives.

The dataset presents opportunities for subsequent research in auto-generative domains such as trailer synthesis and AI-driven film editing. Future extensions might incorporate more diverse cinematic styles or explore comparative cultural analyses across movies from different regions and times. Moreover, enabling more sophisticated models that handle the multimodal nature of movies could lead to breakthroughs in comprehensive AI-driven video understanding.

In conclusion, MovieNet marks a significant step toward advancing AI's ability to understand cinematic content holistically, laying the groundwork for future explorations in artificial intelligence and machine learning applications within the field of multimedia content.