Update README.md

README.md

@@ -18,9 +18,10 @@ A Swin-L image feature model. Superwisely pre-trained on animal re-identificatio
   - Params (M): 228.8
   - Image size: 384 x 384
   - Architecture: swin_large_patch4_window12_384
-- **Papers:**
-  - Swin Transformer: Hierarchical Vision Transformer using Shifted Windows --> https://arxiv.org/abs/2103.14030
-- **Original:** ??
+- **Paper:** [WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification](https://openaccess.thecvf.com/content/WACV2024/html/Cermak_WildlifeDatasets_An_Open-Source_Toolkit_for_Animal_Re-Identification_WACV_2024_paper.html)
+- **Related Papers:**
+  - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
+  - [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/pdf/2304.07193.pdf)
 - **Pretrain Dataset:** All available re-identification datasets --> https://github.com/WildlifeDatasets/wildlife-datasets
 
 ## Model Usage
@@ -52,5 +53,11 @@ output = model(train_transforms(img).unsqueeze(0))  # output is (batch_size, num
 ## Citation
 
 ```bibtex
-TBD
+@inproceedings{vcermak2024wildlifedatasets,
+  title={WildlifeDatasets: An open-source toolkit for animal re-identification},
+  author={{\v{C}}erm{\'a}k, Vojt{\v{e}}ch and Picek, Lukas and Adam, Luk{\'a}{\v{s}} and Papafitsoros, Kostas},
+  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
+  pages={5953--5963},
+  year={2024}
+}
 ```