diff --git a/README.md b/README.md
index 0dd846a..2455501 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
 <div align="center">
 <h2>Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data</h2>
 
-[**Lihe Yang**](https://liheyoung.github.io/)<sup>1</sup> · [**Bingyi Kang**](https://scholar.google.com/citations?user=NmHgX-wAAAAJ)<sup>2+</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup> · [**Xiaogang Xu**](https://xiaogang00.github.io/)<sup>3,4</sup>, [**Jiashi Feng**](https://sites.google.com/site/jshfeng/)<sup>2</sup> · [**Hengshuang Zhao**](https://hszhao.github.io/)<sup>1+</sup>
+[**Lihe Yang**](https://liheyoung.github.io/)<sup>1</sup> · [**Bingyi Kang**](https://scholar.google.com/citations?user=NmHgX-wAAAAJ)<sup>2+</sup> · [**Zilong Huang**](http://speedinghzl.github.io/)<sup>2</sup> · [**Xiaogang Xu**](https://xiaogang00.github.io/)<sup>3,4</sup> · [**Jiashi Feng**](https://sites.google.com/site/jshfeng/)<sup>2</sup> · [**Hengshuang Zhao**](https://hszhao.github.io/)<sup>1+</sup>
 
 <sup>1</sup>The University of Hong Kong · <sup>2</sup>TikTok · <sup>3</sup>Zhejiang Lab · <sup>4</sup>Zhejiang University
 
 <sup>+</sup>corresponding authors
 
-<a href=""><img src='https://img.shields.io/badge/arXiv-Depth Anything-red' alt='Paper PDF'></a>
+<a href="https://arxiv.org/abs/2401.10891"><img src='https://img.shields.io/badge/arXiv-Depth Anything-red' alt='Paper PDF'></a>
 <a href='https://depth-anything.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything-green' alt='Project Page'></a>
 <a href='https://huggingface.co/spaces/LiheYoung/Depth-Anything'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
 
@@ -46,7 +46,7 @@ This work presents Depth Anything, a highly practical solution for robust monocu
 
 Here we compare our Depth Anything with the previously best MiDaS v3.1 BEiT<sub>L-512</sub> model.
 
-Please note that the latest MiDaS is also trained on KITTI and NYUv2, while we do not.
+Please note that the latest MiDaS is also trained on KITTI and NYUv2, while we are not.
 
 | Method | Params | KITTI || NYUv2 || Sintel || DDAD || ETH3D || DIODE ||
 |-|-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
@@ -99,7 +99,7 @@ python run.py --encoder vitl --load-from checkpoints/depth_anything_vitl14.pth -
 If you want to use Depth Anything in our own project, you can simply follow [``run.py``](run.py) to load our models and define data pre-processing. 
 
 <details>
-<summary>Code snippet (note the difference between our data pre-processing and that of MiDaS.)</summary>
+<summary>Code snippet (note the difference between our data pre-processing and that of MiDaS)</summary>
 
 ```python
 from depth_anything.dpt import DPT_DINOv2
@@ -142,7 +142,7 @@ If you find this project useful, please consider citing:
 @article{depthanything,
       title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, 
       author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
-      journal={arXiv:},
+      journal={arXiv:2401.10891},
       year={2024},
 }
 ```
\ No newline at end of file
diff --git a/metric_depth/README.md b/metric_depth/README.md
index e812f97..709453e 100644
--- a/metric_depth/README.md
+++ b/metric_depth/README.md
@@ -82,7 +82,7 @@ If you find this project useful, please consider citing:
 @article{depthanything,
       title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, 
       author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
-      journal={arXiv:},
+      journal={arXiv:2401.10891},
       year={2024},
 }
 ```
\ No newline at end of file