Titlebook: Structural, Syntactic, and Statistical Pattern Recognition; Joint IAPR Internati Adam Krzyzak,Ching Y. Suen,Nicola Nobile Conference procee

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,Graph Reduction Neural Networks for?Structural Pattern Recognition,nherently complex, making graphs the representation formalism of choice. Actually, graphs endow us with both representational power and flexibility. On the other hand, methods for graph-based data typically have high algorithmic complexity hampering their application in domains that comprise large e

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Spatio-Temporal United Memory for Video Anomaly Detection,tion, in which there are considerable difference in each other. From the perspective of philosophy, “act according to circumstances”, we propose a dual-flow network to dissociate appearance information and motion information, processing these information in two individual branches. In addition, we e

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,Learning Distances Between Graph Nodes and?Edges,characterised by nodes that represent chemical elements and edges that represent bonds between them. Given this representation, applications such as drug discovery (graph generation), toxicity prediction (graph regression) or drug analysis (graph classification) can be developed. In all of these app

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,A Novel Graph Kernel Based on?the?Wasserstein Distance and?Spectral Signatures, propose to use two widely used spectral signatures, the Heat Kernel Signature and the Wave Kernel Signature, to create node embeddings able to capture local and global structural information for a given graph. For each node, we concatenate its structural embedding with the one-hot encoding vector o

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,Discovering Respects for?Visual Similarity,measuring it between concepts or images remains challenging. Fortunately, measuring similarity is comparable to answering “.”/“.” two stimuli are similar. While most related works done in computer sciences try to measure the similarity, we propose to analyze it from a different angle and retrospecti