12,607 research outputs found
Deep Structured Features for Semantic Segmentation
We propose a highly structured neural network architecture for semantic
segmentation with an extremely small model size, suitable for low-power
embedded and mobile platforms. Specifically, our architecture combines i) a
Haar wavelet-based tree-like convolutional neural network (CNN), ii) a random
layer realizing a radial basis function kernel approximation, and iii) a linear
classifier. While stages i) and ii) are completely pre-specified, only the
linear classifier is learned from data. We apply the proposed architecture to
outdoor scene and aerial image semantic segmentation and show that the accuracy
of our architecture is competitive with conventional pixel classification CNNs.
Furthermore, we demonstrate that the proposed architecture is data efficient in
the sense of matching the accuracy of pixel classification CNNs when trained on
a much smaller data set.Comment: EUSIPCO 2017, 5 pages, 2 figure
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model
geometric transformations due to the fixed geometric structures in its building
modules. In this work, we introduce two new modules to enhance the
transformation modeling capacity of CNNs, namely, deformable convolution and
deformable RoI pooling. Both are based on the idea of augmenting the spatial
sampling locations in the modules with additional offsets and learning the
offsets from target tasks, without additional supervision. The new modules can
readily replace their plain counterparts in existing CNNs and can be easily
trained end-to-end by standard back-propagation, giving rise to deformable
convolutional networks. Extensive experiments validate the effectiveness of our
approach on sophisticated vision tasks of object detection and semantic
segmentation. The code would be released
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