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|Title:||DeepSetNet: Predicting sets with deep neural networks|
|Citation:||Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), 2017 / vol.2017, pp.5257-5266|
|Publisher Place:||Piscataway, NJ|
|Series/Report no.:||IEEE International Conference on Computer Vision|
|Conference Name:||IEEE International Conference on Computer Vision (ICCV 2017) (22 Oct 2017 - 29 Oct 2017 : Venice, ITALY)|
|S. Hamid Rezatofighi, Vijay Kumar B G, Anton Milan, Ehsan Abbasnejad, Anthony Dick, Ian Reid|
|Abstract:||This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets.|
|Rights:||© 2017 IEEE|
|Appears in Collections:||Computer Science publications|
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