Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model

This repository contains the reference code for computing SAM (Saliency Attentive Model) saliency maps based on the following paper:

Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara
Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
IEEE Transactions on Image Processing, 2018

Please cite with the following Bibtex code:

@article{cornia2018predicting,
  author = {Cornia, Marcella and Baraldi, Lorenzo and Serra, Giuseppe and Cucchiara, Rita},
  title = {{Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model}},
  journal = {IEEE Transactions on Image Processing},
  volume={27},
  number={10},
  pages={5142--5154},
  year = {2018}
}

The PDF of the article is available at this link.

Additional experimental results are reported in the following short paper:

Marcella Cornia, Lorenzo Baraldi, Giuseppe Serra, Rita Cucchiara
SAM: Pushing the Limits of Saliency Prediction Models
Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition Workshops, 2018

Please cite with the following Bibtex code:

@inproceedings{cornia2018sam,
  author = {Cornia, Marcella and Baraldi, Lorenzo and Serra, Giuseppe and Cucchiara, Rita},
  title = {{SAM: Pushing the Limits of Saliency Prediction Models}},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision and Pattern Recognition Workshops},
  year = {2018}
}

Abstract

Data-driven saliency has recently gained a lot of attention thanks to the use of Convolutional Neural Networks for predicting gaze fixations. In this paper we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and we present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a Convolutional LSTM that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. Additionally, to tackle the center bias present in human eye fixations, our model can learn a set of prior maps generated with Gaussian functions. We show, through an extensive evaluation, that the proposed architecture overcomes the current state of the art on two public saliency prediction datasets. We further study the contribution of each key components to demonstrate their robustness on different scenarios.

Requirements

• Python 3.9
• OpenCV 4.6.0
• tensorflow/keras 2.9.0

Setting up the environment

Keras (WINDOWS)

1. Go to %USERPROFILE% directory on Windows and create a folder .keras.
2. Inside it, create keras.json with the following content -

Keras (LINUX)

cd ~/.keras
echo '{"floatx": "float32",\
"epsilon": 1e-07,\
"backend": "tensorflow",\
"image_data_format": "channels_first"}' > keras.json

Usage

from sam import SalMap
salmap = SalMap()
salmap.compile()
salmap.load_weights(weights_path="/weights/sam-resnet_salicon_weights.pkl")
salmap.test(samples_dir="/samples")

Pretrained Models

Download one of the following pretrained models and save it in the code folder:

• SAM-VGG trained on SALICON (2015 release): sam-vgg_salicon_weights.pkl
• SAM-ResNet trained on SALICON (2015 release): sam-resnet_salicon_weights.pkl
• SAM-ResNet trained on SALICON (2017 release): sam-resnet_salicon2017_weights.pkl

Precomputed Saliency Maps

We provide saliency maps predicted by SAM-VGG and SAM-ResNet for three standard datasets (SALICON, MIT1003 and CAT2000):

• SAM-VGG predictions
• SAM-ResNet predictions

In addition, we provide saliency maps predicted by SAM-ResNet on the new release of the SALICON dataset:

• SAM-ResNet predictions (SALICON 2017)

Contact

For more datails about our research please visit our page.

If you have any general doubt about our work, please use the public issues section on this github repo. Alternatively, drop us an e-mail at marcella.cornia@unimore.it or lorenzo.baraldi@unimore.it.