2014 OverFeatIntegratedRecognitionLo
- (Sermanet et al., 2014) ⇒ Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, and Yann LeCun. (2014). “OverFeat: Integrated Recognition, Localization and Detection Using Convolutional Networks.” In: International Conference on Learning Representations (ICLR 2014).
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Notes
Cited By
2014
- (Razavian et al., 2014) ⇒ Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. (2014). “CNN Features Off-the-Shelf: An Astounding Baseline for Recognition.” In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. ISBN:978-1-4799-4308-1 doi:10.1109/CVPRW.2014.131
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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 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.
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2014 OverFeatIntegratedRecognitionLo | Xiang Zhang Yann LeCun Pierre Sermanet David Eigen Michael Mathieu Rob Fergus | OverFeat: Integrated Recognition, Localization and Detection Using Convolutional Networks | 2014 |