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	Author	Santiago Segui; Oriol Pujol; Jordi Vitria
	Title	Learning to count with deep object features			Type	Conference Article
	Year	2015	Publication	Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop	Abbreviated Journal
	Volume		Issue		Pages	90-96
	Keywords
	Abstract	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.
	Address	Boston; USA; June 2015
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	CVPRW
	Notes	MILAB; HuPBA; OR;MV			Approved	no
	Call Number	Admin @ si @ SPV2015			Serial	2636
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