Cumulative Nets for Edge Detection

Abstract

Lots of recent progress have been made by using Convolutional Neural Networks (CNN) for edge detection. Due to the nature of hierarchical representations learned in CNN, it is intuitive to design side networks utilizing the richer convolutional features to improve the edge detection. However, different side networks are isolated, and the final results are usually weighted sum of the side outputs with uneven qualities. To tackle these issues, we propose a Cumulative Network (C-Net), which learns the side network cumulatively based on current visual features and low-level side outputs, to gradually remove detailed or sharp boundaries to enable high-resolution and accurate edge detection. Therefore, the lower-level edge information is cumulatively inherited while the superfluous details are progressively abandoned. In fact, recursively Learningwhere to remove superfluous details from the current edge map with the supervision of a higher-level visual feature is challenging. Furthermore, we employ atrous convolution (AC) and atrous convolution pyramid pooling (ASPP) to robustly detect object boundaries at multiple scales and aspect ratios. Also, cumulatively refining edges using high-level visual information and lower-lever edge maps is achieved by our designed cumulative residual attention (CRA) block. Experimental results show that our C-Net sets new records for edge detection on both two benchmark datasets: BSDS500 (i.e., .819 ODS, .835 OIS and .862 AP) and NYUDV2 (i.e., .762 ODS, .781 OIS, .797 AP). C-Net has great potential to be applied to other deep learning based applications, e.g., image classification and segmentation.