Deeply Supervised Salient Object Detection with Short Connections

Abstract

Recent progress on salient object detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and salient object 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 saliency detection is not obvious. In this paper, we propose a new salient object detection method by introducing short connections to the skip-layer structures within the HED architecture. Our framework takes full advantage of multi-level and multi-scale features extracted from FCNs, providing more advanced representations at each layer, a property that is critically needed to perform segment detection. Our method produces state-of-theart results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency (0.08 seconds per image), effectiveness, and simplicity over the existing algorithms. Beyond that, we conduct an exhaustive analysis on the role of training data on performance. Our experimental results provide a more reasonable and powerful training set for future research and fair comparisons.

ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

“…DSS further connects feature maps at different scales before output, as shown in Figure 4b. [29] and DSS [52] have several side output losses and one fuse loss. Our edge structure is much simpler.…”

“…The edge loss reinforced structure in ERN was inspired by HED [29] and DSS [52]. Different architectures are illustrated in Figure 4.…”

“…Moreover, the lower-resolution feature map has poorer spatial accuracy because of pooling operations, as discussed in Section 1. For these reasons, we did not design side output loss in the same way as HED [29] and DSS [52].…”

“…HED [29] and DSS [52] were designed to accomplish one specific task. However, the edge loss reinforced structure within ERN is auxiliary to the semantic segmentation.…”

“…Thus, the loss of HED consists of side output loss and fuse loss. Hou et al [52] further propose a deeply supervised salient object detection method (DSS) by introducing short connections to the skip-layer structures within the HED architecture. Ke et al [53] propose a side-output residual network (SRN) for symmetry detection based on HED architecture.…”