  Recognizing human actions in video sequences, known as Human Action
Recognition (HAR), is a challenging task in pattern recognition. While
Convolutional Neural Networks (ConvNets) have shown remarkable success in image
recognition, they are not always directly applicable to HAR, as temporal
features are critical for accurate classification. In this paper, we propose a
novel dynamic PSO-ConvNet model for learning actions in videos, building on our
recent work in image recognition. Our approach leverages a framework where the
weight vector of each neural network represents the position of a particle in
phase space, and particles share their current weight vectors and gradient
estimates of the Loss function. To extend our approach to video, we integrate
ConvNets with state-of-the-art temporal methods such as Transformer and
Recurrent Neural Networks. Our experimental results on the UCF-101 dataset
demonstrate substantial improvements of up to 9% in accuracy, which confirms
the effectiveness of our proposed method. In addition, we conducted experiments
on larger and more variety of datasets including Kinetics-400 and HMDB-51 and
obtained preference for Collaborative Learning in comparison with
Non-Collaborative Learning (Individual Learning). Overall, our dynamic
PSO-ConvNet model provides a promising direction for improving HAR by better
capturing the spatio-temporal dynamics of human actions in videos. The code is
available at
https://github.com/leonlha/Video-Action-Recognition-Collaborative-Learning-with-Dynamics-via-PSO-ConvNet-Transformer.
