Abstract
In this paper, we establish a baseline for object reflection symmetry detection in complex backgrounds by presenting a new benchmark and an end-to-end deep learning approach, opening up a promising direction for symmetry detection in the wild. The new benchmark, Sym-PASCAL, spans challenges including object diversity, multi-objects, part-invisibility, and various complex backgrounds that are far beyond those in existing datasets. The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual units (RUs) to fit the errors between the object ground-truth symmetry and the side-outputs of multiple stages. By cascading RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple stages to address the challenges of fitting complex output with limited convolutional layers, suppressing the complex backgrounds, and effectively matching object symmetry at different scales. SRN is further upgraded to a multi-task side-output residual network (MT-SRN) for joint symmetry and edge detection, demonstrating its generality to image-to-mask learning tasks. Experimental results validate both the challenging aspects of Sym-PASCAL benchmark related to real-world images and the state-of-the-art performance of the proposed SRN approach.
Abstract (translated)
在本文中，我们通过提出一个新的基准和端到端深度学习方法，为复杂背景下的物体反射对称检测建立基线，为野外对称检测开辟了一个有前景的方向。新的基准测试Sym-PASCAL涵盖了包括对象多样性，多对象，部分不可见性以及远远超出现有数据集的各种复杂背景等挑战。端到端深度学习方法，称为侧输出残余网络（SRN），利用输出残差单位（RU）来拟合对象地面对称性与多级侧向输出之间的误差。 。通过以深度到浅层的方式级联RU，SRN利用多个阶段之间的“流量”错误来解决利用有限卷积层拟合复杂输出，抑制复杂背景以及有效匹配不同尺度的对象对称性的挑战。 SRN进一步升级为多任务侧输出残差网络（MT-SRN），用于联合对称和边缘检测，展示了其对图像到掩模学习任务的通用性。实验结果验证了与真实世界图像相关的Sym-PASCAL基准测试的挑战性方面以及所提出的SRN方法的最新性能。
URL
https://arxiv.org/abs/1807.06621
