Abstract: Remote sensing images (RSIs) spatiotemporal fusion (STF) make a significant contribution to acquisition of RSIs sequence with simultaneously high temporal and spatial resolution, which ...

Abstract: 6G and beyond will fulfill the requirements of a fully connected world and provide ubiquitous wireless connectivity for all. Transformative solutions are expected to drive the surge for ...

Abstract: Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially ...

Abstract: The traditional Rapidly-exploring Random Tree Star (RRT*) suffers from the low path generation efficiency, numerous invalid exploration points, and unsuitability for navigation in unknown ...

Abstract: Computer-aided pathology diagnosis based on whole slide images, which is often formulated as a weakly supervised multiple instance learning (MIL) paradigm. Current approaches generally ...

Abstract: Despite the transformative potential of generative artificial intelligence (AI) systems within enterprise digital platforms, there still exist gaps in understanding the challenges and ...

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: In the realm of computer vision (CV), balancing speed and accuracy remains a significant challenge. Recent efforts have focused on developing lightweight networks that optimize computational ...

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: 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: Graph Convolutional Networks (GCNs) are widely used for skeleton-based action recognition and achieved remarkable performance. Due to the locality of graph convolution, GCNs can only utilize ...

Abstract: The accurate lifetime prediction of lithium-ion batteries (LIBs) is essential to the normal and effective operation of electric devices. However, such estimation faces huge challenges due to ...