Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

Vision transformers have become popular as a possible substitute to convolutional neural
networks (CNNs) for a variety of computer vision applications. These transformers, with their …

Generative Adversarial Networks (GANs) have been very successful for synthesizing the
images in a given dataset. The artificially generated images by GANs are very realistic. The …

Purpose The aim of this study is to offer valuable insights to businesses and facilitate better
understanding on transformer-based models (TBMs), which are among the widely employed …

Generating realistic images based on text descriptions remains challenging in computer
vision. Existing multi-stage generation methods are sufficient to generate high-resolution …

In order to realize the real-time classification and detection of mutton multi-part, this paper
proposes a mutton multi-part classification and detection method based on the Swin …

Cross-modal content generation has become very popular in recent years. To generate high-
quality and realistic content, a variety of methods have been proposed. Among these …

Text-to-video synthesis has garnered significant attention as a challenging task in the
domain of vision computing. With the advent of unsupervised learning techniques, text-to …

In recent times, Generative Adversarial Networks have successfully synthesized images
through text descriptions. In the domain of image processing, deep convolutional generative …

Abstract Controllable Image Synthesis (CIS) is a methodology that allows users to generate
desired images or manipulate specific attributes of images by providing precise input …