Add pipeline tag and improve model card

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the model card by adding the `pipeline_tag: image-segmentation` to the metadata, which helps users discover the model. I've also updated the README to include a structured summary of the paper and links to the official code repository.

README.md

@@ -1,12 +1,32 @@
 ---
 license: apache-2.0
+pipeline_tag: image-segmentation
 ---
 
+# VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation
+
 This model corresponds to the **VQ-Seg training setup on the ACDC dataset**.
 
-The model is introduced and evaluated in the following paper:
+VQ-Seg is the first approach to employ vector quantization (VQ) to discretize the feature space in semi-supervised medical image segmentation. It introduces a controllable Quantized Perturbation Module (QPM) that replaces traditional dropout, enabling effective regularization by shuffling spatial locations of codebook indices.
+
+- **Paper:** [VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation](https://arxiv.org/abs/2601.10124)
+- **Code:** [GitHub - script-Yang/VQ-Seg](https://github.com/script-Yang/VQ-Seg)
+- **Dataset (ACDC-PNG):** [Hugging Face Datasets](https://huggingface.co/datasets/yscript/ACDC-PNG)
+
+## Key Features
+
+- **Quantized Perturbation Module (QPM):** Replaces dropout with a mechanism that shuffles spatial locations of codebook indices for better regularization.
+- **Dual-branch Architecture:** Shares the post-quantization feature space between image reconstruction and segmentation tasks to mitigate information loss.
+- **Post-VQ Feature Adapter (PFA):** Incorporates guidance from a foundation model (DINOv2) to supplement high-level semantic information.
+
+## Citation
 
-**VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation**  
-arXiv: https://arxiv.org/abs/2601.10124
+If you find this work useful in your research, please consider citing:
 
-Check: https://github.com/script-Yang/VQ-Seg
+```bibtex
+@inproceedings{yangvq,
+  title={VQ-Seg: Vector-Quantized Token Perturbation for Semi-Supervised Medical Image Segmentation},
+  author={Yang, Sicheng and Xing, Zhaohu and Zhu, Lei},
+  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
+}
+```