6 papers
Posterior Flow Matching
Chenrui Ma, Xi Xiao, Lin Zhao +4
Flow matching learns how to transport noise toward data, but its mean prediction does not directly report the endpoint uncertainty remaining at an intermediate state. This paper in…
Drift Flow Matching
Chenrui Ma, Xi Xiao, Lin Zhao +3
Iterative generative models such as Flow Matching and Diffusion models have demonstrated strong test-time scaling behavior, where additional inference computation can improve gener…
Learning Straight Flows: Variational Flow Matching for Efficient Generation
Chenrui Ma, Xi Xiao, Tianyang Wang +2
Flow Matching has limited ability in achieving one-step generation due to its reliance on learned curved trajectories. Previous studies have attempted to address this limitation by…
Stochastic Interpolants via Conditional Dependent Coupling
Chenrui Ma, Xi Xiao, Tianyang Wang +2
Existing image generation models face critical challenges regarding the trade-off between computation and fidelity. Specifically, models relying on a pretrained Variational Autoenc…
Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions
Chenrui Ma, Xi Xiao, Tianyang Wang +1
Current text-driven image editing methods typically follow one of two directions: relying on large-scale, high-quality editing pair datasets to improve editing precision and divers…
CAD-VAE: Leveraging Correlation-Aware Latents for Comprehensive Fair Disentanglement
Chenrui Ma, Xi Xiao, Tianyang Wang +2
While deep generative models have significantly advanced representation learning, they may inherit or amplify biases and fairness issues by encoding sensitive attributes alongside…