Scene Boundary Detection: Introduction

The article dives into the importance of scene boundary detection in the context of Netflix's content processing workflows. This task involves identifying the transitions between scenes, crucial for various applications such as video summarization, highlights detection, content-based video retrieval, and video editing.

Leveraging Screenplay Information for Scene Boundary Detection

The first approach presented is a form of weak supervision that leverages auxiliary data in the form of a screenplay. It aligns screenplay text with timed text (closed captions and audio descriptions) and assigns timestamps to the screenplay's scene headers, effectively identifying potential scene boundaries.

• This method utilizes pre-trained sentence-level embeddings for paraphrasing and dynamic time warping (DTW) to handle variations between screenplay and video text.
• The aligned screenplay information can be used to augment audiovisual machine learning models with scene-level embeddings.

Multimodal Sequential Model for Scene Boundary Detection

The second approach introduces a supervised sequential model that utilizes rich, pre-trained shot-level embeddings for scene boundary detection. This approach offers a complementary solution to the screenplay-based method, particularly when screenplay data is not available.

Audiovisual Content Embeddings

The model utilizes a bidirectional GRU (biGRU) architecture that ingests shot representations and predicts whether a shot marks the end of a scene. The key factor in this approach is the use of pre-trained, multimodal shot embeddings, including video and audio embeddings.

• Video embeddings are derived from an in-house model trained on aligned video clips paired with text.
• Audio embeddings are obtained by separating foreground speech from background audio using source separation techniques and embedding the waveforms using wav2vec2.
• Both early and late-stage fusion approaches are explored, with late fusion consistently outperforming early fusion.

Multimodal Scene Boundary Detection: Results and Insights

The article presents encouraging results for both approaches, highlighting the effectiveness of leveraging multiple modalities for scene boundary detection. The results show improvement over the current state-of-the-art baselines, demonstrating the potential of these methods for practical applications.

• The model's performance matches and sometimes surpasses the state-of-the-art when evaluated solely on video modality.
• Adding audio features to the model further improves performance by a considerable margin.
• Late fusion, where audio and video embeddings are concatenated prior to prediction, proves to be more effective than early fusion.

Scene Boundary Detection: Future Directions

The article concludes by outlining potential future directions for this research, focusing on combining the presented approaches and generalizing the outputs across multiple shot-level inference tasks.

• The goal is to integrate screenplay features into a unified model, enabling more comprehensive scene boundary detection.
• Expanding the model's capabilities to encompass other shot-level tasks, such as shot type classification and memorable moments identification, is a key objective.
• This research aims to contribute to the development of general-purpose video understanding models capable of processing long-form content and understanding its narrative structure.