Deeply supervised salient object detection with short connections

TL;DR

This paper introduces a novel deep learning architecture with short connections within the HED framework, significantly improving salient object detection accuracy and efficiency across multiple benchmarks.

Contribution

It proposes a new saliency detection method that integrates short connections into HED, enhancing multi-scale feature extraction and outperforming existing models.

Findings

Achieves state-of-the-art results on 5 benchmarks.

Runs efficiently at 0.15 seconds per image.

Offers a simpler yet more effective approach.

Abstract

Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and saliency detection algorithms developed lately have been mostly based on Fully Convolutional Neural Networks (FCNs). There is still a large room for improvement over the generic FCN models that do not explicitly deal with the scale-space problem. Holistically-Nested Edge Detector (HED) provides a skip-layer structure with deep supervision for edge and boundary detection, but the performance gain of HED on salience detection is not obvious. In this paper, we propose a new method for saliency detection by introducing short connections to the skip-layer structures within the HED architecture. Our framework provides rich multi-scale feature maps at each layer, a property that is critically needed to perform segment detection. Our method produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency (0.15 seconds per image), effectiveness, and simplicity over the existing algorithms.