论文标题
深层分架：高分辨率边界精炼
DeepStrip: High Resolution Boundary Refinement
论文作者
论文摘要
在本文中，我们将高分辨率图像中的边界进行了针对低分辨率掩模的限制。为了记忆和计算效率，我们建议将目标区域转换为带图像并计算带状域中的边界预测。为了检测目标边界，我们提出了一个带有两个预测层的框架。首先，将所有潜在边界预测为初始预测，然后使用选择层来选择目标边界并平滑结果。为了鼓励准确的预测，引入了测量条带域中边界距离的损失。此外，我们强制执行匹配的一致性和与网络的C0连续性正规化，以减少错误警报。对公众和新创建的高分辨率数据集进行了广泛的实验，可以验证我们的方法。
In this paper, we target refining the boundaries in high resolution images given low resolution masks. For memory and computation efficiency, we propose to convert the regions of interest into strip images and compute a boundary prediction in the strip domain. To detect the target boundary, we present a framework with two prediction layers. First, all potential boundaries are predicted as an initial prediction and then a selection layer is used to pick the target boundary and smooth the result. To encourage accurate prediction, a loss which measures the boundary distance in the strip domain is introduced. In addition, we enforce a matching consistency and C0 continuity regularization to the network to reduce false alarms. Extensive experiments on both public and a newly created high resolution dataset strongly validate our approach.
