Abstract: Accurate understanding of 3D objects in complex scenes plays essential roles in the fields of intelligent transportation and autonomous driving technology. Recent deep neural networks have ...
Abstract: This article provides a comprehensive survey of aggregation strategies in federated learning (FL). This decentralized machine learning (ML) paradigm enables multiple clients to ...
Abstract: Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is ...
Abstract: Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly ...
Abstract: This paper presents a novel approach for wireless federated learning (WFL) that, for the first time, enables the aggregation of local models with mild to moderate errors under practical ...
Abstract: Point cloud registration is a fundamental yet challenging task in computer vision and robotics. While framing it as a reconstruction problem has shown promise, traditional reconstruction ...
Abstract: In Federated Learning (FL), the issue of statistical data heterogeneity has been a significant challenge to the field's ongoing development. This problem is further exacerbated when clients' ...
Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc. Given the enormous social impact and the consequent incentives, the ...
Abstract: Colonoscopy is considered the best prevention and control method for colorectal cancer, which suffers extremely high rates of mortality and morbidity. Automated polyp segmentation of ...
Abstract: The concept of visual masking reveals that human visual perception is influenced by content and distortion information. Existing projection-based methods lose depth information and intrinsic ...