IEEE Signal Processing Society

Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have primarily attempted to utilize the information from higher-order neighbors in the graph, involving the incorporation of powers of the shift operator, such as the graph Laplacian or adjacency matrix.

Static meshes with texture maps have attracted considerable attention in both industrial manufacturing and academic research, leading to an urgent requirement for effective and robust objective quality evaluation. However, current model-based static mesh quality metrics (i.e., metrics that directly use the raw data of the static mesh to extract features and predict the quality) have obvious limitations: most of them only consider geometry information, while color information is ignored, and they have strict constraints for the meshes’ geometrical topology.

Image compression distortion can cause performance degradation of machine analysis tasks, therefore recent years have witnessed fast progress in developing deep image compression methods optimized for machine perception. However, the investigation still lacks for saliency segmentation. First, in this paper we propose a deep compression network increasing local signal fidelity of important image pixels for saliency segmentation, which is different from existing methods utilizing the analysis network loss for backward propagation.

Modern integrated circuits (ICs) require a complex, outsourced supply-chain, involving computer-aided design (CAD) tools, expert knowledge, and advanced foundries. This complexity has led to various security threats, such as Trojans inserted by adversaries during outsourcing, but also run-time threats like physical probing. Our proposed design-time solution, DEFense , is an extensible CAD framework for holistic assessment and proactive mitigation of multiple prominent threats.