---
license: mit
pipeline_tag: unconditional-image-generation
tags:
- flow-matching
- image-generation
---

# VeCoR: Velocity Contrastive Regularization for Flow Matching

[**Paper**](https://arxiv.org/abs/2511.18942) | [**Project Page**](https://p458732.github.io/VeCoR_Project_Page/) | [**GitHub**](https://github.com/p458732/VeCoR)

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](https://github.com/p458732/VeCoR) for environment setup, training, and sampling instructions using the provided scripts.

## Citation
```bibtex
@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}, 
}
```