diff --git a/README.rst b/README.rst
index c23b7ae33..e4fbedb17 100755
--- a/README.rst
+++ b/README.rst
@@ -1,5 +1,20 @@
 Torchreid
 ===========
+.. image:: https://img.shields.io/github/license/KaiyangZhou/deep-person-reid
+   :alt: GitHub license
+   :target: https://github.com/KaiyangZhou/deep-person-reid/blob/master/LICENSE
+
+.. image:: https://img.shields.io/github/v/release/KaiyangZhou/deep-person-reid
+   :alt: GitHub release (latest by date)
+
+.. image:: https://img.shields.io/github/stars/KaiyangZhou/deep-person-reid
+   :alt: GitHub stars
+   :target: https://github.com/KaiyangZhou/deep-person-reid/stargazers
+
+.. image:: https://img.shields.io/github/forks/KaiyangZhou/deep-person-reid
+   :alt: GitHub forks
+   :target: https://github.com/KaiyangZhou/deep-person-reid/network
+
 Torchreid is a library built on `PyTorch <https://pytorch.org/>`_ for deep-learning person re-identification.
 
 It features:
@@ -26,6 +41,8 @@ How-to instructions: https://kaiyangzhou.github.io/deep-person-reid/user_guide.
 
 Model zoo: https://kaiyangzhou.github.io/deep-person-reid/MODEL_ZOO.
 
+Tech report: https://arxiv.org/abs/1910.10093.
+
 
 Installation
 ---------------
@@ -248,6 +265,7 @@ ReID-specific models
 - `PCB <https://arxiv.org/abs/1711.09349>`_
 - `MLFN <https://arxiv.org/abs/1803.09132>`_
 - `OSNet <https://arxiv.org/abs/1905.00953>`_
+- `OSNet-AIN <https://arxiv.org/abs/1910.06827>`_
 
 Losses
 ------
@@ -257,13 +275,27 @@ Losses
 
 Citation
 ---------
-If you find this code useful to your research, please cite the following publication.
+If you find this code useful to your research, please cite the following publications.
 
 .. code-block:: bash
+
+    @article{torchreid,
+      title={Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch},
+      author={Zhou, Kaiyang and Xiang, Tao},
+      journal={arXiv preprint arXiv:1910.10093},
+      year={2019}
+    }
     
-    @article{zhou2019osnet,
+    @inproceedings{zhou2019osnet,
       title={Omni-Scale Feature Learning for Person Re-Identification},
       author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
-      journal={arXiv preprint arXiv:1905.00953},
+      booktitle={ICCV},
+      year={2019}
+    }
+
+    @article{zhou2019learning,
+      title={Learning Generalisable Omni-Scale Representations for Person Re-Identification},
+      author={Zhou, Kaiyang and Yang, Yongxin and Cavallaro, Andrea and Xiang, Tao},
+      journal={arXiv preprint arXiv:1910.06827},
       year={2019}
     }
diff --git a/docs/figures/ranked_results.jpg b/docs/figures/ranking_results.jpg
similarity index 100%
rename from docs/figures/ranked_results.jpg
rename to docs/figures/ranking_results.jpg
diff --git a/docs/user_guide.rst b/docs/user_guide.rst
index add1e1e15..8cacc7c25 100644
--- a/docs/user_guide.rst
+++ b/docs/user_guide.rst
@@ -201,11 +201,11 @@ Visualize learning curves with tensorboard
 The ``SummaryWriter()`` for tensorboard will be automatically initialized in ``engine.run()`` when you are training your model. Therefore, you do not need to do extra jobs. After the training is done, the ``*tf.events*`` file will be saved in ``save_dir``. Then, you just call ``tensorboard --logdir=your_save_dir`` in your terminal and visit ``http://localhost:6006/`` in a web browser. See `pytorch tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ for further information.
 
 
-Visualize ranked results
--------------------------
-Ranked images can be visualized by setting ``visrank`` to true in ``engine.run()``. ``visrank_topk`` determines the top-k images to be visualized (Default is ``visrank_topk=10``). Note that ``visrank`` can only be used in test mode, i.e. ``test_only=True`` in ``engine.run()``. The images will be saved under ``save_dir/visrank_DATASETNAME`` where each image contains the top-k ranked list given a query. An example is shown below. Red and green denote incorrect and correct matches respectively.
+Visualize ranking results
+---------------------------
+This can be achieved by setting ``visrank`` to true in ``engine.run()``. ``visrank_topk`` determines the top-k images to be visualized (Default is ``visrank_topk=10``). Note that ``visrank`` can only be used in test mode, i.e. ``test_only=True`` in ``engine.run()``. The output will be saved under ``save_dir/visrank_DATASETNAME`` where each plot contains the top-k similar gallery images given a query. An example is shown below where red and green denote incorrect and correct matches respectively.
 
-.. image:: figures/ranked_results.jpg
+.. image:: figures/ranking_results.jpg
     :width: 800px
     :align: center