From cd27a07f60e1896e93554f4e91152e61cf40b2b2 Mon Sep 17 00:00:00 2001
From: Nataniel Ruiz <nruiz9@gatech.edu>
Date: 星期一, 04 三月 2019 08:13:56 +0800
Subject: [PATCH] Update README.md
---
README.md | 22 +++++++---------------
1 files changed, 7 insertions(+), 15 deletions(-)
diff --git a/README.md b/README.md
index 8dafd94..0009306 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,7 @@
**Hopenet** is an accurate and easy to use head pose estimation network. Models have been trained on the 300W-LP dataset and have been tested on real data with good qualitative performance.
-For details about the method and quantitative results please check the [paper](https://arxiv.org/abs/1710.00925).
+For details about the method and quantitative results please check the CVPR Workshop [paper](https://arxiv.org/abs/1710.00925).
<div align="center">
<img src="conan-cruise.gif" /><br><br>
@@ -50,20 +50,12 @@
If you find Hopenet useful in your research please cite:
```
-@article{DBLP:journals/corr/abs-1710-00925,
- author = {Nataniel Ruiz and
- Eunji Chong and
- James M. Rehg},
- title = {Fine-Grained Head Pose Estimation Without Keypoints},
- journal = {CoRR},
- volume = {abs/1710.00925},
- year = {2017},
- url = {http://arxiv.org/abs/1710.00925},
- archivePrefix = {arXiv},
- eprint = {1710.00925},
- timestamp = {Wed, 01 Nov 2017 19:05:43 +0100},
- biburl = {http://dblp.org/rec/bib/journals/corr/abs-1710-00925},
- bibsource = {dblp computer science bibliography, http://dblp.org}
+@InProceedings{Ruiz_2018_CVPR_Workshops,
+author = {Ruiz, Nataniel and Chong, Eunji and Rehg, James M.},
+title = {Fine-Grained Head Pose Estimation Without Keypoints},
+booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+month = {June},
+year = {2018}
}
```
--
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