Video Action Recognition Collaborative Learning with Dynamics via PSO-ConvNet Transformer

Human Action Recognition (HAR) involves the task of categorizing actions present in video sequences. Although it presents interesting problems, it remains one of the most challenging domains in pattern recognition. Convolutional Neural Networks (ConvNets) have demonstrated exceptional success in image recognition and related areas. However, these advanced techniques are not always directly applicable to HAR, as the consideration of temporal features is crucial. In this paper, we present a dynamic PSO-ConvNet model for learning actions in video, drawing on our recent research in image recognition. Our methods are based on a framework where the weight vector of each neural network serves as the position of a particle in phase space, and particles exchange their current weight vectors and gradient estimates of the Loss function. We extend the approach to video by integrating a ConvNet with state-of-the-art temporal methods such as Transformer and Recurrent Neural Networks. The results reveal substantial advancements, with improvements of up to 9% on UCF-101 dataset. The code is available at https://github.com/leonlha/Video-Action-Recognition-via-PSO-ConvNet-Transformer-Collaborative-Learning-with-Dynamics.