TbsNet: the importance of thin-branch structures in CNNs.

The performance of a convolutional neural network (CNN) model is influenced by several factors, such as depth, width, network structure, size of the receptive field, and feature map scaling. The optimization of the best combination of these factors poses as the main difficulty in designing a viable architecture. This article presents an analysis of key factors influencing network performance, offers several strategies for constructing an efficient convolutional network, and introduces a novel architecture named TbsNet (thin-branch structure network).

Contactless blood oxygen estimation from face videos: A multi-model fusion method based on deep learning.

Blood Oxygen ( ), a key indicator of respiratory function, has received increasing attention during the COVID-19 pandemic. Clinical results show that patients with COVID-19 likely have distinct lower before the onset of significant symptoms. Aiming at the shortcomings of current methods for monitoring by face videos, this paper proposes a novel multi-model fusion method based on deep learning for estimation.