Spatio-Temporal Collaborative Module for Efficient Action Recognition.

Area of Science:

• Computer Vision
• Artificial Intelligence
• Machine Learning

Background:

• Efficient action recognition is crucial for real-world applications but computationally intensive.
• Existing methods often struggle with high complexity due to integrated spatial-temporal computations like 3D convolutions.

Purpose of the Study:

• To propose a novel spatio-temporal collaborative (STC) module for efficient action recognition.
• To explore channel splitting and filter decoupling for efficient architecture design and feature refinement.

Main Methods:

• The STC module splits video feature channels into two groups for parallel spatio-temporal representation learning.
• It utilizes decoupled convolutional operators and two computation-efficient blocks for spatial and temporal feature extraction.
• Features are refined using global temporal or spatial contexts, aggregating information dynamics from respective axes.

Main Results:

• Extensive experiments were conducted on five video benchmark datasets requiring temporal reasoning.
• The proposed STC networks demonstrated a competitive trade-off between model efficiency and recognition effectiveness.
• The method successfully addresses the computational challenges of action recognition.

Conclusions:

• The STC module offers an efficient approach to action recognition by optimizing spatial-temporal feature learning.
• This architecture design effectively reduces computational cost while maintaining high performance.
• The findings suggest STC as a promising direction for developing efficient video analysis systems.