license: mit
pipeline_tag: unconditional-image-generation
tags:
- flow-matching
- image-generation
VeCoR: Velocity Contrastive Regularization for Flow Matching
Paper | Project Page | GitHub
Velocity Contrastive Regularization (VeCoR) is a complementary training scheme for flow-based generative modeling that augments the standard Flow Matching (FM) objective with contrastive, two-sided supervision. VeCoR not only aligns the predicted velocity with a stable reference direction (positive supervision) but also pushes it away from inconsistent, off-manifold directions (negative supervision).
This formulation transforms FM from a purely attractive, one-sided objective into a two-sided training signal, regularizing trajectory evolution and improving perceptual fidelity across datasets and backbones, particularly in low-step and lightweight settings.
Performance
On ImageNet-1K 256x256, VeCoR achieves FID=1.94 (SiT-XL/2 backbone), demonstrating significant gains in stability and image quality. It also shows consistent improvements in MS-COCO text-to-image generation.
Usage
Please refer to the official GitHub repository for environment setup, training, and sampling instructions using the provided scripts.
Citation
@misc{hong2025vecorvelocitycontrastive,
title={VeCoR - Velocity Contrastive Regularization for Flow Matching},
author={Zong-Wei Hong and Jing-lun Li and Lin-Ze Li and Shen Zhang and Yao Tang},
year={2025},
eprint={2511.18942},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2511.18942},
}