Abstract: Existing visual deep learning paradigms, which are based on labels, struggle to capture the intricate interrelationships between farmland and its surrounding environment and fail to account ...

Abstract: To better characterize the differences in category features in Facial Expression Recognition (FER) tasks, and improve inter-class separability and intra-class compactness, we propose a ...

Abstract: In the manufacturing process of hot-rolled steel strips, various mechanical forces, and environmental conditions can cause surface defects, making their detection crucial for ensuring ...

Abstract: Tendon-driven continuum robots are of great promise in dexterous manipulation in long-narrow spaces, such as in-situ maintenance of aeroengines, due to their slender body and compliant hyper ...

Abstract: The rapid advancements in wireless technologies have led to numerous research studies that provide evidence of the successful utilization of wireless signals, particularly, WiFi signals for ...

Abstract: Low Earth Orbit (LEO) satellites have emerged as crucial enablers of direct connections with remote terrestrial terminals. However, energy limitations and insufficient antenna capabilities ...

Abstract: Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering (DC), ...

Abstract: In smart communities, social media allowed users easy access to multimedia content. With recent advancements in computer vision and natural language processing, machine learning (ML), and ...

Abstract: Autonomous driving has achieved significant milestones in research and development over the last two decades. There is increasing interest in the field as the deployment of autonomous ...

Abstract: Semantic segmentation of remote sensing images plays a critical role in areas such as urban change detection, environmental protection, and geohazard identification. Convolutional Neural ...

Abstract: Masked image modeling (MIM) is a highly popular and effective self-supervised learning method for image understanding. The existing MIM-based methods mostly focus on spatial feature modeling ...

Abstract: Recently, the multiscale problem in computer vision has gradually attracted people’s attention. This article focuses on multiscale representation for object detection and recognition, ...