LIGAR: Lightweight General-purpose Action Recognition
This work addresses the need for efficient, general-purpose action recognition models for edge computing applications, though it appears incremental by building on existing architectures.
The authors tackled the challenge of creating a universal, lightweight action recognition model suitable for edge devices by designing a network architecture and training pipeline that works for both appearance-based and motion-based tasks, and they introduced an Adaptive Clip Selection framework to handle label noise, achieving an excellent trade-off between performance and accuracy compared to state-of-the-art solutions.
Growing amount of different practical tasks in a video understanding problem has addressed the great challenge aiming to design an universal solution, which should be available for broad masses and suitable for the demanding edge-oriented inference. In this paper we are focused on designing a network architecture and a training pipeline to tackle the mentioned challenges. Our architecture takes the best from the previous ones and brings the ability to be successful not only in appearance-based action recognition tasks but in motion-based problems too. Furthermore, the induced label noise problem is formulated and Adaptive Clip Selection (ACS) framework is proposed to deal with it. Together it makes the LIGAR framework the general-purpose action recognition solution. We also have reported the extensive analysis on the general and gesture datasets to show the excellent trade-off between the performance and the accuracy in comparison to the state-of-the-art solutions. Training code is available at: https://github.com/openvinotoolkit/training_extensions. For the efficient edge-oriented inference all trained models can be exported into the OpenVINO format.