EDN: Salient Object Detection via Extremely-Downsampled Network

• URL: http://arxiv.org/abs/2012.13093v1
• Date: Thu, 24 Dec 2020 04:23:48 GMT
• Title: EDN: Salient Object Detection via Extremely-Downsampled Network
• Authors: Yu-Huan Wu, Yun Liu, Le Zhang, Ming-Ming Cheng
• Abstract summary: We introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image. Experiments demonstrate that EDN achieves sArt performance with real-time speed.
• Score: 66.38046176176017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent progress on salient object detection (SOD) mainly benefits from multi-scale learning, where the high-level and low-level features work collaboratively in locating salient objects and discovering fine details, respectively. However, most efforts are devoted to low-level feature learning by fusing multi-scale features or enhancing boundary representations. In this paper, we show another direction that improving high-level feature learning is essential for SOD as well. To verify this, we introduce an Extremely-Downsampled Network (EDN), which employs an extreme downsampling technique to effectively learn a global view of the whole image, leading to accurate salient object localization. A novel Scale-Correlated Pyramid Convolution (SCPC) is also designed to build an elegant decoder for recovering object details from the above extreme downsampling. Extensive experiments demonstrate that EDN achieves \sArt performance with real-time speed. Hence, this work is expected to spark some new thinking in SOD. The code will be released.

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