Scalable Object Detection using Deep Neural Networks
Venue
Computer Vision and Pattern Recognition, IEEE (2014), pp. 2155- 2162
Publication Year
2014
Authors
Dumitru Erhan, Christian Szegedy, Alexander Toshev, Dragomir Anguelov
BibTeX
Abstract
Deep convolutional neural networks have recently achieved state-of-the-art
performance on a number of image recognition benchmarks, including the ImageNet
Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the
localization sub-task was a network that predicts a single bounding box and a
confidence score for each object category in the image. Such a model captures the
whole-image context around the objects but cannot handle multiple instances of the
same object in the image without naively replicating the number of outputs for each
instance. In this work, we propose a saliency-inspired neural network model for
detection, which predicts a set of class-agnostic bounding boxes along with a
single score for each box, corresponding to its likelihood of containing any object
of interest. The model naturally handles a variable number of instances for each
class and allows for cross-class generalization at the highest levels of the
network. We are able to obtain competitive recognition performance on VOC2007 and
ILSVRC2012, while using only the top few predicted locations in each image and a
small number of neural network evaluations.
