Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer

We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribut...

• Type: Conference
• Year: 2009
• Where: CVPR
• Authors: Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling
• Keywords: Attribute-based Classification, Classification, Computer Vision, CVPR 2009, Object Detection, Unseen Objects