论文标题
sideinfnet：用于半自动语义分割的深度神经网络，并提供侧面信息
SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information
论文作者
论文摘要
完全自动执行是许多计算机视觉应用程序的最终目标。但是，这个目标在与医疗应用等高失败成本相关的任务中并不总是现实的。对于这些任务，由于理想的准确性和性能，通常首选用户允许用户指导计算机算法的半自动方法。受半自动方法的实用性和适用性的启发，本文提出了一种新颖的深神经网络体系结构，即SideInfnet，可以有效地整合从图像中学到的特征与从用户注释中提取的附带信息。为了评估我们的方法，我们将提出的网络应用于三个语义分割任务，并在基准数据集上进行了广泛的实验。实验结果和与先前工作的比较已经验证了我们的模型的优势，这表明该模型在半自动语义分割中具有一般性和有效性。
Fully-automatic execution is the ultimate goal for many Computer Vision applications. However, this objective is not always realistic in tasks associated with high failure costs, such as medical applications. For these tasks, semi-automatic methods allowing minimal effort from users to guide computer algorithms are often preferred due to desirable accuracy and performance. Inspired by the practicality and applicability of the semi-automatic approach, this paper proposes a novel deep neural network architecture, namely SideInfNet that effectively integrates features learnt from images with side information extracted from user annotations. To evaluate our method, we applied the proposed network to three semantic segmentation tasks and conducted extensive experiments on benchmark datasets. Experimental results and comparison with prior work have verified the superiority of our model, suggesting the generality and effectiveness of the model in semi-automatic semantic segmentation.
