该文研究如何在图片中定位特定属性物体（如“废弃的车”等）。由于一个物体可能包含几十甚至上百个非互斥的属性，训练特定属性物体检测器的难点是为大量的特定属性物体收集训练图片并标定边界框。该文提出使用特定属性物体分类器扩展物体检测器获取特定属性物体检测器的方法。其中的特定属性物体分类器通过使用从互联网上挖掘的图片以及从物体检测器和自然语言处理工具获取的标注信息训练得到。构建了特定属性物体检测数据库并对特定属性物体检测器的性能进行分析，结果表明：特定属性检测器的平均精度均值比物体检测器相对提高30%。
This paper addresses the problem of localizing an attributed object, such as "abandoned car", in images. Since one object may have tens or even hundreds of non-exclusive attributes, the main difficulties of attributed object detection are manually collecting training images and labeling the bounding boxes for a large number of attributed objects. This attributed object detector extends the object detector with an attributed object classifier. The attributed object classifier is trained by images from the Internet and labeling information gathered by the object detector and a natural language processing tool. An attributed object detection dataset was developed to evaluate the attributed object detectors. Tests show that this attributed object detector has good performance gains of 30% for the mean average precision compared to generic object detectors.
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