Graph signal processing tasks that leverage spectral information typically assume access to the complete graph topology, which is often unavailable in practice. We propose a systematic framework for subgraph filter learning (SFL), where subgraph-supported operators approximate ambient graph filters under partial observ...
Purui Zhang, Feng Ji, Yanan Zhao et al.· arXiv.org· 0 citations
By modeling the graph structure as a distribution conditioned on signal realizations, this framework provides a principled approach to signal-dependent graph structures, which are common in real-world applications, while explicitly encoding uncertainty in graph topology.
Yanan Zhao, Feng Ji, Xingchao Jian et al.· 0 citations
Results show that although current models achieve strong semantic adherence and visual quality, they often fail to faithfully capture fine-grained cultural details, particularly for underrepresented regions, rituals, and multimodal cultural cues.
Xian-Jing Han, Yuhan Su, Yang Deng et al.· 2 citations
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