Straight-Path Flow Matching for Incomplete Multi-View Clustering

2026-07-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, short

Summary

Straight-Path Flow Matching for Incomplete Multi-View Clustering introduces a novel flow-matching framework to address the challenge of clustering multi-modal data with missing views. Unlike prior end-to-end generative methods that use diffusion models and stochastic noise-to-data trajectories, this approach employs a linear interpolation path between paired view representations, replacing diffusion with deterministic probability flows. A formal analysis demonstrates that these ODE flows are inherently better suited for clustering objectives, preserving class-conditional data distributions and cluster consistency. The proposed end-to-end IMVC architecture integrates this straight-path flow-matching view completion with cluster-level and entropy-based alignment. This framework achieved new state-of-the-art performance on standard IMVC benchmarks and was accepted to ECCV 2026.

Key takeaway

For Machine Learning Engineers developing solutions for multi-modal data with missing components, consider adopting flow-matching techniques over traditional diffusion models. Your systems can achieve superior clustering performance by leveraging deterministic ODE flows, which are shown to better preserve class-conditional data distributions and maintain cluster consistency. This approach offers a more robust and accurate method for handling incomplete multi-view datasets.

Key insights

Deterministic ODE flows in flow-matching enhance incomplete multi-view clustering by preserving class-conditional data distributions.

Principles

• Deterministic ODE flows align better with clustering.
• Maintain cluster consistency in finite-step regimes.
• Respect class-conditional data distributions.

Method

The method uses a flow-matching framework with a linear interpolation path, replacing diffusion with probability flows between observed and missing views. It integrates view completion with cluster-level and entropy-based alignment.

In practice

• Implement flow-matching for view completion.
• Align clusters using entropy-based methods.

Topics

• Incomplete Multi-View Clustering
• Flow Matching
• Diffusion Models
• Deterministic ODE Flows
• Multi-modal Data
• Clustering Consistency

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.