We present an integrated framework for using Convolutional Networks for classification, localization and detection. We show how a multiscale and sliding window approach can be efficiently implemented within a ConvNet. We also introduce a novel deep learning approach to localization by learning to predict object boundaries. Bounding boxes are then accumulated rather than suppressed in order to increase detection confidence. We show that different tasks can be learned simultaneously using a single shared network. This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks. In post-competition work, we establish a new state of the art for the detection task. Finally, we release a feature extractor from our best model called OverFeat.
The first work about object detection and classification based on deep learning has been done in 2013; Sermanet et al. REF present an integrated framework for using deep learning for object detection, localization, and classification; this framework obtains very competitive results for the detection and classifications tasks.
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
ICLR 2014
arXiv: Computer Vision and Pattern Recognition
Computer Science