论文标题
通过深厚的社区学习弱监督的实例细分
Weakly Supervised Instance Segmentation by Deep Community Learning
论文作者
论文摘要
我们提出了一种基于多个任务的深入社区学习的弱监督实例细分算法。该任务是作为弱监督的对象检测和语义分割的组合，其中分别识别和分割了同一类的单个对象。我们通过设计一个统一的深神经网络体系结构来解决此问题，该统一的对象检测具有积极的反馈回路，并具有边界框回归，实例掩码生成，实例分割和特征提取。网络的每个组件都与他人进行主动交互以提高准确性，并且模型的端到端训练性使我们的结果更加稳健和可重复。拟议的算法在弱监督的设置中实现了最先进的性能，而没有任何其他培训，例如标准基准数据集中的快速R-CNN和蒙版R-CNN。我们的算法的实现可在项目网页上获得：https：//cv.snu.ac.kr/research/wsis_cl。
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks. This task is formulated as a combination of weakly supervised object detection and semantic segmentation, where individual objects of the same class are identified and segmented separately. We address this problem by designing a unified deep neural network architecture, which has a positive feedback loop of object detection with bounding box regression, instance mask generation, instance segmentation, and feature extraction. Each component of the network makes active interactions with others to improve accuracy, and the end-to-end trainability of our model makes our results more robust and reproducible. The proposed algorithm achieves state-of-the-art performance in the weakly supervised setting without any additional training such as Fast R-CNN and Mask R-CNN on the standard benchmark dataset. The implementation of our algorithm is available on the project webpage: https://cv.snu.ac.kr/research/WSIS_CL.
