Attention-based Assisted Excitation for Salient Object Detection

TL;DR

This paper introduces an attention-inspired mechanism to enhance salient object detection in CNNs by exciting object regions in feature maps, inspired by human visual cortex, improving boundary and interior object segmentation.

Contribution

The paper proposes a novel attention-based activation excitation mechanism for CNNs, inspired by brain processes, to improve salient object detection performance.

Findings

Significant improvement in mean absolute error and F-measure.

Better boundary and interior object segmentation.

Effective across multiple CNN architectures.

Abstract

Visual attention brings significant progress for Convolution Neural Networks (CNNs) in various applications. In this paper, object-based attention in human visual cortex inspires us to introduce a mechanism for modification of activations in feature maps of CNNs. In this mechanism, the activations of object locations are excited in feature maps. This mechanism is specifically inspired by attention-based gain modulation in object-based attention in brain. It facilitates figure-ground segregation in the visual cortex. Similar to brain, we use the idea to address two challenges in salient object detection: gathering object interior parts while segregation from background with concise boundaries. We implement the object-based attention in the U-net model using different architectures in the encoder parts, including AlexNet, VGG, and ResNet. The proposed method was examined on three benchmark datasets: HKU-IS, MSRB, and PASCAL-S. Experimental results showed that our inspired method could significantly improve the results in terms of mean absolute error and F-measure. The results also showed that our proposed method better captured not only the boundary but also the object interior. Thus, it can tackle the mentioned challenges.