论文标题
GRAFTNET：CNN的工程实施，用于细粒度的多标签任务
GraftNet: An Engineering Implementation of CNN for Fine-grained Multi-label Task
论文作者
论文摘要
事实证明，具有分支机构的多标签网络在准确性和速度方面都表现良好，但是由于对注释和培训的重新工作效率低，因此在向新标签上提供动态扩展方面缺乏灵活性。对于多标签分类任务，要涵盖新标签，我们不仅需要注释新收集的图像，而且还需要注释以前的整个数据集来检查这些新标签的存在。还要对整个重新注销的数据集进行培训花费很多时间。为了更有效，准确地识别新标签，我们提出了GraftNet，它是一个可自定义的树状网络，其中继线具有用于通用特征提取的动态图，并且分支在带有单个标签的子数据库上分别训练，以提高精度。 GraftNet可以降低成本，提高灵活性并逐步处理新标签。实验结果表明，它在我们的人类属性识别任务上具有良好的性能，这是精细的多标签分类。
Multi-label networks with branches are proved to perform well in both accuracy and speed, but lacks flexibility in providing dynamic extension onto new labels due to the low efficiency of re-work on annotating and training. For multi-label classification task, to cover new labels we need to annotate not only newly collected images, but also the previous whole dataset to check presence of these new labels. Also training on whole re-annotated dataset costs much time. In order to recognize new labels more effectively and accurately, we propose GraftNet, which is a customizable tree-like network with its trunk pretrained with a dynamic graph for generic feature extraction, and branches separately trained on sub-datasets with single label to improve accuracy. GraftNet could reduce cost, increase flexibility, and incrementally handle new labels. Experimental results show that it has good performance on our human attributes recognition task, which is fine-grained multi-label classification.
