V-CAST: Curvature-Aware Spatiotemporal Pruning Technology for Efficient Video Large Language Models

V-CAST: Curvature-Aware Spatiotemporal Pruning Technology

V-CAST is an innovative pruning method for video large language models, designed to address the computational efficiency challenges posed by the spatiotemporal characteristics of video data. By identifying key spatiotemporal regions through a curvature-aware mechanism, it significantly reduces computational costs while maintaining model performance, providing a feasible path for real-time video understanding applications. Its core lies in a three-layer collaborative pruning architecture, combining lightweight curvature calculation and dynamic strategies, with excellent experimentally verified results.

Background: Efficiency Bottlenecks of Video Large Models

Video Large Language Models (Video LLMs) exhibit strong capabilities in fields such as video question answering and action recognition. However, the spatiotemporal characteristics of video data lead to computational challenges—short videos contain hundreds of frames, and direct processing easily causes memory explosion and inference delays. Traditional compression methods are designed for static images or text, making it difficult to capture the temporal dependencies of videos. How to reduce overhead while maintaining spatiotemporal modeling capabilities has become a key issue for deployment.

Core Ideas and Technical Mechanisms

The core insight of V-CAST is the unevenness of video content in spatiotemporal dimensions. It introduces 'curvature' as a spatiotemporal importance metric (high curvature corresponds to key regions such as motion boundaries and scene transitions). Its pruning architecture includes three layers:

1. Spatial Pruning: Locate key visual regions in a single frame and focus resources on foreground objects;
2. Temporal Pruning: Identify key frames and skip low-information transition frames;
3. Spatiotemporal Joint Pruning: Construct a unified spatiotemporal curvature tensor to capture the temporal evolution of spatial features and avoid loss of coherence.

Implementation Details: Lightweight and Dynamic Pruning

To reduce pruning overhead, V-CAST uses an efficient curvature estimation algorithm: insert lightweight modules in the shallow feature extraction stage, approximate curvature through the local change rate of feature vectors, without the need for complete forward propagation. It also adopts a dynamic pruning strategy, adaptively adjusting the pruning ratio according to video complexity—reduce pruning intensity for complex videos and aggressively compress simple videos, achieving 'small overhead for large savings'.

Experimental Verification: Balance Between Efficiency and Accuracy

In video understanding benchmark tests, V-CAST maintains over 95% of the original accuracy while reducing the number of inference floating-point operations by more than 60%. It has strong generalization ability, stably identifying key regions in academic datasets and real-world scenarios with excellent robustness. Compared with static pruning methods, the curvature-aware mechanism can effectively filter noisy motions (such as camera shake) and focus on meaningful visual changes.

Application Prospects and Open Source Value

The open-source V-CAST provides an efficiency optimization tool for the community: researchers can explore the sparsity of video models, and engineers can directly integrate it into inference pipelines to achieve significant acceleration. In future edge-side deployments (autonomous driving, mobile AR, real-time analysis), model efficiency is crucial, and V-CAST's curvature-aware paradigm is expected to become a standard component of video AI systems.