We present cVIL, a class-centric approach to visual interactive labeling, which facilitates human annotation of large and complex image data sets. cVIL uses different property measures to support instance labeling for labeling difficult instances and batch labeling to quickly label easy instances. Simulated experiments reveal that cVIL with batch labeling can outperform traditional labeling approaches based on active learning. In a user study, cVIL led to better accuracy and higher user preference compared to a traditional instance-based visual interactive labeling approach based on 2D scatterplots.

翻译：我们提出cVIL，一种以类别为中心的视觉交互式标注方法，旨在促进大规模复杂图像数据集的人工标注。cVIL利用多种属性度量，支持对困难样本进行实例标注，并通过批量标注快速标记简单样本。模拟实验表明，采用批量标注的cVIL能够超越基于主动学习的传统标注方法。在用户研究中，与基于二维散点图的传统实例级视觉交互式标注方法相比，cVIL实现了更高的准确率和更强的用户偏好。