论文标题
博览会 - 从极端点开始的软聚焦生成器和对稳健对象分割的关注
FAIRS -- Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points
论文作者
论文摘要
已经对用户输入的语义分割进行了积极研究，以促进用于数据注释和其他应用程序的交互式分割。最近的研究表明，极端点可以有效地用于编码用户输入。从极端点产生的热图可以附加到RGB图像中，并输入训练模型。在这项研究中，我们提出了博览会 - 一种新方法，以极端点和纠正键的形式从用户输入中生成对象分割。我们提出了一种新颖的方法，可以通过新颖且可扩展的方式有效地从极端点和纠正键单击来编码用户输入，该方法使网络可以使用可变数量的点击次数，包括纠正键单击以进行输出细化。我们还将双重注意模块与我们的方法相结合，以提高模型优先处理对象的功效。我们证明，这些添加有助于从用户输入，在多个大规模数据集中从用户输入中的密集对象细分中实现重大改进。通过实验，我们证明了我们方法生成高质量培训数据的能力，以及其以原则上的方式纳入极端点，引导点击和纠正键的能力。
Semantic segmentation from user inputs has been actively studied to facilitate interactive segmentation for data annotation and other applications. Recent studies have shown that extreme points can be effectively used to encode user inputs. A heat map generated from the extreme points can be appended to the RGB image and input to the model for training. In this study, we present FAIRS -- a new approach to generate object segmentation from user inputs in the form of extreme points and corrective clicks. We propose a novel approach for effectively encoding the user input from extreme points and corrective clicks, in a novel and scalable manner that allows the network to work with a variable number of clicks, including corrective clicks for output refinement. We also integrate a dual attention module with our approach to increase the efficacy of the model in preferentially attending to the objects. We demonstrate that these additions help achieve significant improvements over state-of-the-art in dense object segmentation from user inputs, on multiple large-scale datasets. Through experiments, we demonstrate our method's ability to generate high-quality training data as well as its scalability in incorporating extreme points, guiding clicks, and corrective clicks in a principled manner.
