← 研踪 PaperTrailCVF · CVPR 2026:cvf:6d353b2f3cee830873b3a6338f476cfd · 记录 #1
Flow Matching for Multimodal Distributions
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 01:39
摘要
Recently, vision foundation models have been shown to boost the efficiency of flow-based generative models by revealing the intrinsic union-of-manifold structures and lowering the complexity of the latent/target distribution. In this paper, we exploit the multimodality aspect of the union-of-manifold structures, and aim to further improve the learning and inference efficiency for flow-matching models. To this end, we propose an efficient source and coupling co-design method termed Mixture-Modeling Flow Matching (MM-FM), by integrating a data-adaptive multimodal source distribution (implemented as Gaussian mixture models) and mode-dependent data coupling. The former shortens the distance between the source and the target, and the latter promotes local and straighter flows. We also derive theoretical results to confirm our intuition in a quantitative sense. In our experiments on ImageNet256x256 with multimodal DINOv2-B latents, MM-FM exhibits superior learning efficiency and state-of-the-art unconditional generation quality: FID=2.74 with autoguidance in only 80 epochs.
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记录 #1 · 2026 · 最新观测版本
Flow Matching for Multimodal Distributions
Recently, vision foundation models have been shown to boost the efficiency of flow-based generative models by revealing the intrinsic union-of-manifold structures and lowering the complexity of the latent/target distribution. In this paper, we exploit the multimodality aspect of the union-of-manifold structures, and aim to further improve the learning and inference efficiency for flow-matching models. To this end, we propose an efficient source and coupling co-design method termed Mixture-Modeling Flow Matching (MM-FM), by integrating a data-adaptive multimodal source distribution (implemented as Gaussian mixture models) and mode-dependent data coupling. The former shortens the distance between the source and the target, and the latter promotes local and straighter flows. We also derive theoretical results to confirm our intuition in a quantitative sense. In our experiments on ImageNet256x256 with multimodal DINOv2-B latents, MM-FM exhibits superior learning efficiency and state-of-the-art unconditional generation quality: FID=2.74 with autoguidance in only 80 epochs.
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