Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural network in order to understand their underlying representation. To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training. We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene.
Our proposal is related to different techniques. On one hand we may find work on multiple instance learning related to similar concepts. Multiple instance learning @cite_11 is a variation of supervised learning where instead of having individually labeled examples, instances comes in bags. These bags contain multiple instances. A bag is labeled positive if there is at least one example with the concept of interest, or labeled negative otherwise. The positive bag can be regarded as a set of attracting instances and the negative one as a set of repulsive instances. The classifier tries to learn a common concept across all the instances of the positive bag. As we commented in the introduction this approach is commonly found in large scale computer vision under the name of weakly supervised learning.
@cite_11
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Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.