Deriving Explanation of Deep Visual Saliency Models

TL;DR

This paper introduces a method to interpret deep neural network-based visual saliency models by reconstructing their activation maps using biologically inspired filters, enhancing understanding of their learning mechanisms.

Contribution

It presents a novel technique to derive explainable saliency models from deep networks, applicable across different architectures, and evaluates their interpretability and performance on benchmark datasets.

Findings

Reconstructed activation maps align with human perception theories.

Explainable models achieve comparable performance to original deep models.

The approach is generic and applicable to various deep saliency architectures.

Abstract

Deep neural networks have shown their profound impact on achieving human level performance in visual saliency prediction. However, it is still unclear how they learn the task and what it means in terms of understanding human visual system. In this work, we develop a technique to derive explainable saliency models from their corresponding deep neural architecture based saliency models by applying human perception theories and the conventional concepts of saliency. This technique helps us understand the learning pattern of the deep network at its intermediate layers through their activation maps. Initially, we consider two state-of-the-art deep saliency models, namely UNISAL and MSI-Net for our interpretation. We use a set of biologically plausible log-gabor filters for identifying and reconstructing the activation maps of them using our explainable saliency model. The final saliency map is generated using these reconstructed activation maps. We also build our own deep saliency model named cross-concatenated multi-scale residual block based network (CMRNet) for saliency prediction. Then, we evaluate and compare the performance of the explainable models derived from UNISAL, MSI-Net and CMRNet on three benchmark datasets with other state-of-the-art methods. Hence, we propose that this approach of explainability can be applied to any deep visual saliency model for interpretation which makes it a generic one.