# Deep Visual Attention Prediction

## TL;DR

This paper introduces a deep learning model that predicts human eye fixation on scenes by effectively leveraging multi-scale features through a hierarchical, multi-level supervision approach, achieving state-of-the-art results.

## Contribution

The work presents a novel skip-layer deep network with deep supervision for multi-scale saliency prediction, improving upon prior CNN-based attention models.

## Key findings

- Achieves state-of-the-art performance on benchmark datasets.
- Reduces redundancy by integrating multi-level predictions in a single network.
- Maintains competitive inference speed.

## Abstract

In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.