Classes in natural images tend to follow long tail distributions. This is problematic when there are insufficient training examples for rare classes. This effect is emphasized in compound classes, involving the conjunction of several concepts, such as those appearing in action-recognition datasets. In this paper, we propose to address this issue by learning how to utilize common visual concepts which are readily available. We detect the presence of prominent concepts in images and use them to infer the target labels instead of using visual features directly, combining tools from vision and natural-language processing. We validate our method on the recently introduced HICO dataset reaching a mAP of 31.54% and on the Stanford-40 Actions dataset, where the proposed method outperforms current state-of-the art and, combined with direct visual features, obtains an accuracy 83.12%. Moreover, the method provides for each class a semantically meaningful list of keywords and relevant image regions relating it to its constituent concepts.

本研究使用自然语言处理技术检测图像中突出的概念，从而代替直接使用视觉特征来推断目标标签，并在 HICO 数据集和 Stanford-40 Actions 数据集上验证，精确度分别达到了 31.54% 和 83.12%，同时为每个类别提供了语义上有意义的关键词列表和相关图像区域。

基于概念和属性的动作分类