Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model
This work addresses saliency prediction for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the problem of predicting human eye fixations by introducing a saliency attentive model that uses a Convolutional LSTM and neural attentive mechanisms to iteratively refine saliency maps, outperforming the current state of the art on public datasets.
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 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 typical of 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 outperforms the current state of the art on public saliency prediction datasets. We further study the contribution of each key component to demonstrate their robustness on different scenarios.