OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Authors: Michael Mathieu, Yann LeCun, Rob Fergus, David Eigen, Pierre Sermanet, Xiang Zhang
ICLR 2014

Abstract: 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 learnt 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 produced near state of the art results for the detection and classifications tasks. Finally, we release a feature extractor from our best model called OverFeat.


Peer review:
[Review]:
It is a very nice work and I enjoy reading the paper. Now I believe the era of 'deformable part model' (by Felzenszwalb, McAllester, Ramanan et al) in CV detection will find its successor soon. We will witness another revolution in the field of object detection after talking about DPM for 5 years. 

One comment of the paper: Is it possible to know the results of applying Overfeat to PASCAL detection dataset? I am also interested the comparison with NEC's Regionlets (No.2 place in ImageNet 2013 detection) on PASCAL. 

But even without results on PASCAL, this paper still deserves an acceptance from any conference.