---
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},
}
```