One-shot Learning - Background

Background

As with most classification schemes, one-shot learning involves three main challenges: "

• Representation: How should we model objects and categories?

• Learning: How may we acquire such models?

• Recognition: Given a new image, how do we detect the presence of a known object/category amongst clutter, and despite occlusion, viewpoint, and lighting changes?"

However, one-shot learning differs greatly from single object recognition and even standard category recognition algorithms is in its emphasis on the principle of knowledge transfer, which encapsulates prior knowledge of learnt categories and allows for learning on minimal training examples.

• Knowledge transfer by model parameters: One set of algorithms for one-shot learning achieves knowledge transfer through the reuse of model parameters, often exploiting the similarity between previously learned classes and the new object classes to be learned. Classes of objects are first learned on numerous training examples (i.e. not in a one-shot fashion), then new object classes are learned using transformations of model parameters from the previously learnt classes or selection relevant parameters for a classifier as in M. Fink, 2004.
• Knowledge transfer by sharing features: Another class of algorithms achieves knowledge transfer by sharing parts or features of objects across classes. In a paper presented at CVPR 2005 by Bart and Ullman, an algorithm extracts "diagnostic information" in patches from already learnt classes by maximizing the patches' mutual information, and then applies these features to the learning of a new class. A dog class, for example, may be learned in one shot from previous knowledge of horse and cow classes, because dog objects may contain similar distinguishing patches.
• Knowledge transfer by contextual information: Whereas the previous two groups of knowledge transfer work in one-shot learning relied on the similarity between new object classes and the previously learned classes on which they were based, transfer by contextual information instead appeals to global knowledge of the scene in which the object is placed. A paper presented at NIPS 2004 by K. Murphy et al. uses such global information as frequency distributions in a conditional random field framework to recognize objects. Another algorithm by D. Hoiem et al. makes use of contextual information in the form of camera height and scene geometry to prune object detection. Algorithms of this type have two advantages. First, they should be able to learn object classes which are relatively dissimilar in visual appearance; and second, they should perform well precisely in situations where an image has not been hand-cropped and carefully aligned, but rather which naturally occur.