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Evaluating multi-class learning strategies in a generative hierarchical framework for object detection
Sanja Fidler · Marko Boben · Ales Leonardis

Wed Dec 09 07:00 PM -- 11:59 PM (PST) @

Multiple object class learning and detection is a challenging problem due to the large number of object classes and their high visual variability. Specialized detectors usually excel in performance, while joint representations optimize sharing and reduce inference time --- but are complex to train. Conveniently, sequential learning of categories cuts down training time by transferring existing knowledge to novel classes, but cannot fully exploit the richness of shareability and might depend on ordering in learning. In hierarchical frameworks these issues have been little explored. In this paper, we show how different types of multi-class learning can be done within one generative hierarchical framework and provide a rigorous experimental analysis of various object class learning strategies as the number of classes grows. Specifically, we propose, evaluate and compare three important types of multi-class learning: 1.) independent training of individual categories, 2.) joint training of classes, 3.) sequential learning of classes. We explore and compare their computational behavior (space and time) and detection performance as a function of the number of learned classes on several recognition data sets.

Author Information

Sanja Fidler (University of Toronto)
Marko Boben
Ales Leonardis (University of Ljubljana)

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