Updata Citation

README.md

@@ -4,8 +4,18 @@
     <img src="crowdpose.gif", width="360">
 </p>
 
+## Citation
+If you find our works useful in your reasearch, please consider citing:
+
+    @article{li2018crowdpose,
+      title={CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark},
+      author={Li, Jiefeng and Wang, Can and Zhu, Hao and Mao, Yihuan and Fang, Hao-Shu and Lu, Cewu},
+      journal={arXiv preprint arXiv:1812.00324},
+      year={2018}
+    }
+
 ## Introduction
-This is the official repo of [CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark](https://arxiv.org/abs/1812.00324). Our proposed method surpasses the state-of-the-art methods on CrowdPose dataset by **5** mAP and results on MSCOCO dataset demonstrate the generalization ability of our method (comparatively **0.8** mAP higher). Images in our proposed CrowdPose dataset have a uniform distribution of *Crowd Index* among \[0, 1\].
+This is the official repo of **CVPR2019** paper [*CrowdPose: Efficient Crowded Scenes Pose Estimation and A New Benchmark*](https://arxiv.org/abs/1812.00324). Our proposed method surpasses the state-of-the-art methods on CrowdPose dataset by **5** mAP and results on MSCOCO dataset demonstrate the generalization ability of our method (comparatively **0.8** mAP higher). Images in our proposed CrowdPose dataset have a uniform distribution of *Crowd Index* among \[0, 1\].
 
 ## Code
 We provide [evaluation tools](crowdpose-api) for CrowdPose dataset. Our evaluation tools is developed based on [@cocodataset/cocoapi](https://github.com/cocodataset/cocoapi). The source code of our model has been integrated into [AlphaPose](https://github.com/MVIG-SJTU/AlphaPose/tree/pytorch).