Object-Plane Co-Represented and Graph Propagation-Based Semantic Descriptor for Relocalization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Robotics and Automation Letters"
DOI: 10.1109/lra.2022.3191056
Abstract: Relocalization is a critical component of robotics applications, it poses challenges due to changes in lighting conditions, weather, and viewing point. Image feature-based approaches are appearance-sensitive, high-level semantic landmark-based methods are ambiguous, and topological map… read more here.
Keywords: relocalization; object plane; graph; graph propagation ... See more keywords
LapGLP: Approximating Infinite-Layer Graph Convolutions With Laplacian for Federated Recommendation
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2025.3594145
Abstract: Recommender systems (RSs) have become crucial in helping users navigate the vast amount of online content available today. Graph neural networks (GNNs) have been applied to RSs to capture complex user–item relationships, but existing methods… read more here.
Keywords: approximating infinite; graph propagation; privacy; lapglp approximating ... See more keywords
GPENs: Graph Data Learning With Graph Propagation-Embedding Networks.
Sign Up to like & getrecommendations! Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3120100
Abstract: Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured data representation and learning tasks, such as graph… read more here.
Keywords: representation; propagation; graph propagation; propagation embedding ... See more keywords