Straight-Path Flow Matching for Incomplete Multi-View Clustering
Incomplete Multi-View Clustering addresses the problem of clustering multi-modal data when certain views are missing. Recent end-to-end generative approaches leverage diffusion models to recover missing views via stochastic noise-to-data trajectories. While expressive, such mechanisms are not explicitly designed for clustering, as they initialize from cluster-agnostic noise and rely on stochastic denoising dynamics. In this work, we revisit probability path design in end-to-end generative IMVC. We introduce a flow-matching framework with a linear interpolation path between paired view represen
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- FuzzySimilar name plus overlapping authors · 67%bytedance/deer-flow →
“Title similarity 0.73; shared authors: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%mudler/LocalAI →
“Shared author/contributor keys: guo”
- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Yiteng Yuan →
“Straight-Path Flow Matching for Incomplete Multi-View Clustering”
- LinkedLinked via arxiv author · 85%Junyan Wang →
“Straight-Path Flow Matching for Incomplete Multi-View Clustering”
- LinkedLinked via arxiv author · 85%Zheyuan Liu →
“Straight-Path Flow Matching for Incomplete Multi-View Clustering”
- LinkedLinked via arxiv author · 85%Hong Jia →
“Straight-Path Flow Matching for Incomplete Multi-View Clustering”