Built independently by an author, for readers. Read the story and support ChapterPal
ChapterPal Logo
Log inSign up

keyword

movie scene segmentation

Movie scene segmentation is the automated video analysis process of dividing a full-length film into distinct, semantically coherent narrative units known as scenes. While a movie is physically composed of individual shots representing continuous camera recordings, a scene encompasses a series of consecutive shots unified by a common location, time, topic, or dramatic event. To detect these high-level boundaries, algorithms analyze visual features, audio tracks, and textual subtitles to capture both immediate visual transitions and long-range narrative relationships across shots. This task is fundamental for structuring long-form cinematic content, enabling downstream applications such as video summarization, content-based retrieval, recommendation systems, and storyline comprehension.

1 item

Efficient Movie Scene Detection using State-Space Transformers

Efficient Movie Scene Detection using State-Space Transformers

Md Mohaiminul Islam, Mahmudul Hasan, Kishan Shamsundar Athrey, Tony Braskich, Gedas Bertasius

OrganizationsComcastUniversity of North Carolina at Chapel Hill

Why you should read this

Proposes TranS4mer, a hybrid architecture combining structured state-space sequence modeling with self-attention to detect movie scene boundaries across long video sequences while cutting GPU memory usage by three times compared to standard transformers.

The ability to distinguish between different movie scenes is critical for understanding the storyline of a movie. However, accurately detecting movie scenes is often challenging as it requires the ability to reason over very long movie segments. This contrasts with most existing video recognition models, which are typically designed for short-range video analysis. This work proposes a State-Space Transformer model that can efficiently capture dependencies in long movie videos for accurate movie scene detection. Our model, called TranS4mer, is built using a novel S4A building block, combining the strengths of structured state-space sequence (S4) and self-attention (A) layers. Given a sequence of frames divided into movie shots (uninterrupted periods where the camera position does not change), the S4A block first applies self-attention to capture short-range intra-shot dependencies. Afterward, the state-space operation in the S4A block aggregates long-range inter-shot cues. The final TranS4mer model, which can be trained end-to-end, is obtained by stacking the S4A blocks one after the other multiple times. Our proposed TranS4mer outperforms all prior methods in three movie scene detection datasets, including MovieNet, BBC, and OVSD, while being 2× faster and requiring 3× less GPU memory than standard Transformer models. We will release our code and models.

Added

2026-09-26