Update README.md

README.md

@@ -12,7 +12,7 @@ A dataset for visual similarity tasks, that includes various zero-shot and train
 Visual similarity measures the perceptual agreement between two objects based on their visual appearance. Two objects can be similar or dissimilar based on their color, shape, size, pattern, utility, and more. In fact, all of these factors and many others take part in determining the degree of visual similarity between two objects with varying importance. Therefore, defining the perceived visual simi- larity based on these factors is challenging. Nonetheless, learning visual similarities is a key building block for many practical utilities such as search, recommendations, etc.
 
 - **Repository: https://github.com/vsd-benchmark/vsd** 
-- **Paper: [TBD]** 
+- **Paper: https://arxiv.org/abs/2308.14753** 
 - **Leaderboard: https://vsd-benchmark.github.io/vsd/** 
 
 ### Dataset Summary
@@ -71,12 +71,17 @@ Instead of using 'datasets' loader, you can download the annotation files direct
 
 ### License
 
-VSD Fashion is licensed under MIT License. The released models can be used for commercial purposes free of charge.
+VSD Fashion is licensed under MIT License.
 
 ### Citation Information
 
-[More Information Needed]
-
-### Contributions
-
-[More Information Needed]
+```
+@misc{barkan2023efficient,
+  title={Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond}, 
+  author={Oren Barkan and Tal Reiss and Jonathan Weill and Ori Katz and Roy Hirsch and Itzik Malkiel and Noam Koenigstein},
+  year={2023},
+  eprint={2308.14753},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+```