Update README.md

README.md

@@ -13,6 +13,6 @@ MSI-Net
 
 # Summary
 
-MSI-Net is a visual saliency model that predicts where humans fixate on natural images using a contextual encoder-decoder network trained on eye movement data. The model is based on a convolutional neural network architecture and includes an ASPP module with multiple convolutional layers at different dilation rates to capture multi-scale features in parallel. Moreover, it combines the resulting representations with global scene information towards accurate predictions of visual saliency. MSI-Net consists of roughly 25M parameters and thus presents a suitable choice for applications with limited computational resources. For more information on the model, check out [GitHub](https://github.com/alexanderkroner/saliency) and the corresponding [paper](https://doi.org/10.1016/j.neunet.2020.05.004).
+MSI-Net is a visual saliency model that predicts where humans fixate on natural images using a contextual encoder-decoder network trained on eye movement data. The model is based on a convolutional neural network architecture and includes an ASPP module with multiple convolutional layers at different dilation rates to capture multi-scale features in parallel. Moreover, it combines the resulting representations with global scene information towards accurate predictions of visual saliency. MSI-Net consists of roughly 25M parameters and thus presents a suitable choice for applications with limited computational resources. For more information on the model, check out [GitHub](https://github.com/alexanderkroner/saliency) and the corresponding [paper](https://doi.org/10.1016/j.neunet.2020.05.004) or [preprint](https://arxiv.org/abs/1902.06634).
 
 <img src="https://github.com/alexanderkroner/saliency/blob/master/figures/architecture.jpg?raw=true" width="700">