update reference

README.rst

@@ -33,6 +33,7 @@ You can find some research projects that are built on top of Torchreid `here <ht
 
 What's new
 ------------
+- [Mar 2021] `OSNet <https://arxiv.org/abs/1910.06827>`_ will appear in the TPAMI journal! Compared with the conference version, which focuses on discriminative feature learning using the omni-scale building block, this journal extension further considers generalizable feature learning by integrating `instance normalization layers <https://arxiv.org/abs/1607.08022>`_ with the OSNet architecture. We hope this journal paper can motivate more future work to taclke the generalization issue in cross-dataset re-ID.
 - [Mar 2021] Generalization across domains (datasets) in person re-ID is crucial in real-world applications, which is closely related to the topic of *domain generalization*. Interested in learning how the field of domain generalization has developed over the last decade? Check our recent survey in this topic at https://arxiv.org/abs/2103.02503, with coverage on the history, datasets, related problems, methodologies, potential directions, and so on (*methods designed for generalizable re-ID are also covered*!).
 - [Feb 2021] ``v1.3.6`` Added `University-1652 <https://dl.acm.org/doi/abs/10.1145/3394171.3413896>`_, a new dataset for multi-view multi-source geo-localization (credit to `Zhedong Zheng <https://github.com/layumi>`_).
 - [Feb 2021] ``v1.3.5``: Now the `cython code <https://github.com/KaiyangZhou/deep-person-reid/pull/412>`_ works on Windows (credit to `lablabla <https://github.com/lablabla>`_).
@@ -303,9 +304,9 @@ If you find this code useful to your research, please cite the following papers.
       year={2019}
     }
 
-    @article{zhou2019learning,
+    @article{zhou2021osnet,
       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}
+      journal={TPAMI},
+      year={2021}
     }

docs/AWESOME_REID.md

@@ -2,9 +2,9 @@
 Here is a collection of ReID-related research with links to papers and code. You are welcome to submit [PR](https://help.github.com/articles/creating-a-pull-request/)s if you find something missing.
 
 
-- [ArXiv20] Deep Learning for Person Re-identification: A Survey and Outlook [[paper](https://arxiv.org/abs/2001.04193)] [[code](https://github.com/mangye16/ReID-Survey)]
+- [TPAMI21] Learning Generalisable Omni-Scale Representations for Person Re-Identification [[paper](https://arxiv.org/abs/1910.06827)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
 
-- [ArXiv19] Learning Generalisable Omni-Scale Representations for Person Re-Identification [[paper](https://arxiv.org/abs/1910.06827)][[code](https://github.com/KaiyangZhou/deep-person-reid)]
+- [TPAMI21] Deep Learning for Person Re-identification: A Survey and Outlook [[paper](https://arxiv.org/abs/2001.04193)] [[code](https://github.com/mangye16/ReID-Survey)]
 
 - [ICCV19] RGB-Infrared Cross-Modality Person Re-Identification via Joint Pixel and Feature Alignment. [[paper](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_RGB-Infrared_Cross-Modality_Person_Re-Identification_via_Joint_Pixel_and_Feature_Alignment_ICCV_2019_paper.pdf)] [[code](https://github.com/wangguanan/AlignGAN)]
 

projects/OSNet_AIN/README.md

@@ -22,11 +22,11 @@ The default config was designed for 8 Tesla V100 32GB GPUs. You can modify the b
 ## Citation
 If you find this code useful to your research, please consider citing the following papers.
 ```
-@article{zhou2019learning,
+@article{zhou2021osnet,
   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}
+  journal={TPAMI},
+  year={2021}
 }
 
 @inproceedings{zhou2019osnet,

projects/OSNet_AIN/osnet_child.py

@@ -367,7 +367,7 @@ class OSNet(nn.Module):
     Reference:
         - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
         - Zhou et al. Learning Generalisable Omni-Scale Representations
-          for Person Re-Identification. arXiv preprint, 2019.
+          for Person Re-Identification. TPAMI, 2021.
     """
 
     def __init__(

projects/OSNet_AIN/osnet_search.py

@@ -436,7 +436,7 @@ class OSNet(nn.Module):
     Reference:
         - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
         - Zhou et al. Learning Generalisable Omni-Scale Representations
-          for Person Re-Identification. arXiv preprint, 2019.
+          for Person Re-Identification. TPAMI, 2021.
     """
 
     def __init__(

torchreid/data/transforms.py

@@ -154,7 +154,7 @@ class RandomPatch(object):
     Reference:
         - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
         - Zhou et al. Learning Generalisable Omni-Scale Representations
-          for Person Re-Identification. arXiv preprint, 2019.
+          for Person Re-Identification. TPAMI, 2021.
     """
 
     def __init__(

torchreid/models/osnet.py

@@ -285,7 +285,7 @@ class OSNet(nn.Module):
     Reference:
         - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
         - Zhou et al. Learning Generalisable Omni-Scale Representations
-          for Person Re-Identification. arXiv preprint, 2019.
+          for Person Re-Identification. TPAMI, 2021.
     """
 
     def __init__(

torchreid/models/osnet_ain.py

@@ -304,7 +304,7 @@ class OSNet(nn.Module):
     Reference:
         - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.
         - Zhou et al. Learning Generalisable Omni-Scale Representations
-          for Person Re-Identification. arXiv preprint, 2019.
+          for Person Re-Identification. TPAMI, 2021.
     """
 
     def __init__(