  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.
