Add model card info and metadata (#1)

- Add model card info and metadata (6ea0fa84ec980971f0eab1dd7ff75e1148853e41)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,33 @@
----
-license: mit
----
+---
+license: mit
+pipeline_tag: image-feature-extraction
+---
+
+# T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability
+
+T-REN (**T**ext-aligned **R**egion **E**ncoder **N**etwork) is an efficient encoder that maps visual data to a compact set of text-aligned region-level representations (region tokens). It is built on top of a frozen [DINOv3](https://github.com/facebookresearch/dinov3) ViT-L/16 backbone and adds only 3.7% additional parameters.
+
+Compared to patch-based vision-language backbones, T-REN yields stronger dense cross-modal understanding while significantly reducing token counts by more than 24x for images and 187x for videos.
+
+- **Paper:** [T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability](https://huggingface.co/papers/2604.18573)
+- **GitHub Repository:** [savya08/T-REN](https://github.com/savya08/T-REN)
+
+## Highlights
+
+Specifically, T-REN delivers:
+- **+5.9 mIoU** on ADE20K open-vocabulary segmentation.
+- **+18.4% recall** on COCO object-level text-image retrieval.
+- **+15.6% recall** on Ego4D video object localization.
+- **+17.6% mIoU** on VSPW video scene parsing.
+
+## Citation
+
+```bibtex
+@misc{khosla2026tren,
+      title={T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability}, 
+      author={Savya Khosla and Sethuraman T V and Aryan Chadha and Alexander Schwing and Derek Hoiem},
+      year={2026},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+}
+```