keyword

state-space transformers

A state-space transformer is a hybrid deep learning architecture that combines self-attention mechanisms with structured state-space sequence models to process long sequential data efficiently. While conventional transformers rely on standard attention layers that scale quadratically with sequence length, state-space transformers integrate continuous-time or discretized state-space operations to capture long-range dependencies with lower computational and memory complexity. Within these architectures, self-attention layers are typically utilized to capture fine-grained, local or short-range context, while state-space layers aggregate broad, long-range temporal or structural cues. This integrated approach allows the model to retain the representational expressiveness of transformer attention while scaling effectively to very long sequences across domains such as video analysis, audio processing, and long-context language modeling.

1 item

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.

Source

https://arxiv.org/pdf/2212.14427

Added

2026-09-26