Salient Object Detection via High-to-Low Hierarchical Context Aggregation

TL;DR

This paper introduces a high-to-low hierarchical context aggregation method using an hourglass network with intermediate supervision, significantly improving salient object detection accuracy with a simpler approach.

Contribution

It proposes a novel high-to-low hierarchical context aggregation framework with an hourglass network for salient object detection, simplifying fusion strategies while enhancing performance.

Findings

Achieves state-of-the-art results on six challenging datasets.

Uses a high-to-low self-learning approach for contextual feature extraction.

Demonstrates effectiveness of hierarchical context aggregation in saliency detection.

Abstract

Recent progress on salient object detection mainly aims at exploiting how to effectively integrate convolutional side-output features in convolutional neural networks (CNN). Based on this, most of the existing state-of-the-art saliency detectors design complex network structures to fuse the side-output features of the backbone feature extraction networks. However, should the fusion strategies be more and more complex for accurate salient object detection? In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features. As we know, the contexts of an image usually refer to the global structures, and the top layers of CNN usually learn to convey global information. On the other hand, it is difficult for the intermediate side-output features to express contextual information. Here, we design an hourglass network with intermediate supervision to learn contextual features in a high-to-low manner. The learned hierarchical contexts are aggregated to generate the hybrid contextual expression for an input image. At last, the hybrid contextual features can be used for accurate saliency estimation. We extensively evaluate our method on six challenging saliency datasets, and our simple method achieves state-of-the-art performance under various evaluation metrics. Code will be released upon paper acceptance.