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README.md

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+---
+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}, 
+}
+```