The Transformer–convolutional neural network (CNN) hybrid learning approach is gaining
traction for balancing deep and shallow image features for hierarchical semantic …

Machine learning (ML) has become a popular technique for various automation tasks in the
era of Industry 4.0, such as the analysis and synthesis of visual data such as images and …

Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging
the correlation between the auditory and visual modalities. Despite their many useful …

In this paper, we explore a novel Text-supervised Egocentic Semantic Segmentation (TESS)
task that aims to assign pixel-level categories to egocentric images weakly supervised by …

The text-to-image task, a critical branch of computer vision and image processing, has
witnessed remarkable advancements fueled by the abundance of realistic data and rapid AI …

Mobile robot navigation in unknown, dynamic, hostile and/or crowded environments is a
challenging task, especially when it comes to multi-robot systems. Looking at the issue from …

The modern field of machine learning has made remarkable progress, and the demand for
training data required by machine learning systems is constantly increasing. However, the …

Background: In recent years, computer vision has undergone significant advancements,
revolutionizing fields such as robotics, augmented reality, and autonomoussystems. Key to …

Deep neural networks usually have many hyperparameters that need to be tuned. Modern
material design problems usually require material scientists to sequentially select …