InBRwSANet: Self-attention based parallel inverted residual bottleneck architecture for human action recognition in smart cities.

ABSTRACT: Human Action Recognition (HAR) has grown significantly because of its many uses, including real-time surveillance and human-computer interaction. Various variations in routine human actions make the recognition process of action more difficult. In this paper, we proposed a novel deep learning architecture known as Inverted Bottleneck Residual with Self-Attention (InBRwSA). The proposed architecture is based on two different modules. In the first module, 6-parallel inverted bottleneck residual blocks are designed, and each block is connected with a skip connection. These blocks aim to learn complex human actions in many convolutional layers. After that, the second module is designed based on the self-attention mechanism. The learned weights of the first module are passed to self-attention,

SUBMITTER: Jadoon YK 

PROVIDER: S-EPMC12111635 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature