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We study a multiclass multiple instance learning (MIL) problem where thelabels only suggest whether any instance of a class exists or does not exist ina training sample or example. No further information, e.g., the number ofinstances of each class, relative locations or orders of all instances in atraining sample, is exploited. Such a weak supervision learning problem can beexactly solved by maximizing the model likelihood fitting given observations,and finds applications to tasks like multiple object detection and localizationfor image understanding. We discuss its relationship to the classicclassification problem, the traditional MIL, and connectionist temporalclassification (CTC). We use image recognition as the example task to developour method, although it is applicable to data with higher or lower dimensionswithout much modification. Experimental results show that our method can beused to learn all convolutional neural networks for solving real-world multipleobject detection and localization tasks with weak annotations, e.g.,transcribing house number sequences from the Google street view imagerydataset.

A Multiclass Multiple Instance Learning Method with Exact Likelihood