Generative Adversarial Networks (GAN) show excellent performance in various problems of
computer vision, computer graphics, and machine learning, but require large amounts of …

Deep learning technologies have significantly advanced the field of single-image super-resolution
(SISR), yet existing methods often prioritize peak signal-to-noise ratio (PSNR) over …

In this study, we propose EnhanceCenter, a multiple-object tracking model that
demonstrates enhanced tracking efficiency and stability while reducing dependencies on …

In the field of computer vision, convolutional neural network (CNN)-based models have
demonstrated high accuracy and good generalization performance. However, in semantic …

Many mobile devices compress images excessively to meet limited bandwidth requirements
and adopt the Block-based Discrete Cosine Transform (BDCT) coding structure. This …

Object detection automatically identifies and locates specific objects within images or videos,
and plays a critical role in various fields. Among the many approaches, the YOLO model …

Vision Transformer (ViT) has been proposed as a new image recognition method in the field
of computer vision. ViT applies a Transformer structure with excellent performance in the …

This paper presents a crowd-density simulation system developed using the Unity engine to
predict and prevent crowd-crush accidents. Crowd-crush accidents can occur in various …

This paper set out to revise and improve existing autonomous driving models using reinforcement
learning, thus proposing a reinforced autonomous driving prediction model. The paper …

There is no doubt that CNN has made remarkable technological developments as the core
technology of computer vision, but the pooling technique used for CNN has its own issues. …