The first task is object recognition across variations of viewing conditions (such as viewpoint). Experience with familiar objects of a certain class (such as faces or cars) is used to facilitate generalization to previously unseen views of novel objects of the same class. In the resulting scheme, a face that has only been seen in a frontal view is successfully recognized in profile. Pose, illumination, and other viewing conditions are handled in a single general framework.
The second task is object classification. The goal here is to observe a single instance of a novel class, and to generalize to additional instances of this class. Experience with already learned classes is used to facilitate this generalization. Both high-level data (on the level of entire classes) and middle-level data (on the level of individual features) help improve generalization. Combining the two sources of information further improves the performance.
Joint work with Shimon Ullman.
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