EGNet:Edge Guidance Network for Salient Object Detection
arXiv:1908.08297
Abstract
Fully convolutional neural networks (FCNs) have shown their advantages in the salient object detection task. However, most existing FCNs-based methods still suffer from coarse object boundaries. In this paper, to solve this problem, we focus on the complementarity between salient edge information and salient object information. Accordingly, we present an edge guidance network (EGNet) for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network. In the first step, we extract the salient object features by a progressive fusion way. In the second step, we integrate the local edge information and global location information to obtain the salient edge features. Finally, to sufficiently leverage these complementary features, we couple the same salient edge features with salient object features at various resolutions. Benefiting from the rich edge information and location information in salient edge features, the fused features can help locate salient objects, especially their boundaries more accurately. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on six widely used datasets without any pre-processing and post-processing. The source code is available at http: //mmcheng.net/egnet/.
References in corpus (8)
- Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
- Deeply supervised salient object detection with short connections
- RANet: Ranking Attention Network for Fast Video Object Segmentation
- Salient Object Detection with Lossless Feature Reflection and Weighted Structural Loss
- Deep Edge-Aware Saliency Detection
- Richer and Deeper Supervision Network for Salient Object Detection
- Deep Reasoning with Multi-Scale Context for Salient Object Detection
- FLIC: Fast Linear Iterative Clustering with Active Search