This is the official PyTorch implementation of the paper Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks by:
The paper presents a saliency-based distortion layer for convolutional neural networks that helps to improve the spatial sampling of input data for a given task. For instance, for the eye-tracking task and the fine-grain classification task, the layer produces deformed images such as:
The implementation has been tested wihth PyTorch 0.4.0 but it is likely to work on previous version of PyTorch as well.
To add a Saliency Sampler layer at the beginning of your model, you just need to define a task network and a saliency network and instantiate the model as:
task_network = resnet101()
saliency_network = saliency_network_resnet18()
task_input_size = 224
saliency_input_size = 224
model = Saliency_Sampler(task_network,saliency_network,task_input_size,saliency_input_size)
For the reader's reference, in main.py we provide an example of a ResNet-101 network trained with the ImageNet dataset. Since the images in the ImageNet dataset are not particularly high resolution, the saliency sampler improves the task network performance only marginally. However, when used with datasets with higher resolution images (such as the iNaturalist, GazeCapture and many others), the Saliency Sampler significantly boosts the performance of the task network.
If you want to cite our research, please use:
@inproceedings{recasens2018learning,
title={Learning to Zoom: a Saliency-Based Sampling Layer for Neural Networks},
author={Recasens, Adria and Kellnhofer, Petr and Stent, Simon and Matusik, Wojciech and Torralba, Antonio},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
pages={51--66},
year={2018}
}