Spaces:
Running
Running
| title: Visual Saliency Prediction | |
| emoji: ⚡ | |
| colorFrom: pink | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.23.1 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| This demo is based on the implementation of MSI-Net (multi-scale information network), as described in the Neural Networks paper [Contextual encoder-decoder network for visual saliency prediction](https://doi.org/10.1016/j.neunet.2020.05.004) (2020) and on [arXiv](https://arxiv.org/abs/1902.06634). If you find the model or results useful, please cite the following paper: | |
| ``` | |
| @article{kroner2020contextual, | |
| title={Contextual encoder-decoder network for visual saliency prediction}, | |
| author={Kroner, Alexander and Senden, Mario and Driessens, Kurt and Goebel, Rainer}, | |
| url={http://www.sciencedirect.com/science/article/pii/S0893608020301660}, | |
| doi={https://doi.org/10.1016/j.neunet.2020.05.004}, | |
| journal={Neural Networks}, | |
| publisher={Elsevier}, | |
| year={2020}, | |
| volume={129}, | |
| pages={261--270}, | |
| issn={0893-6080} | |
| } | |
| ``` |