797 research outputs found
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
Irregular Convolutional Neural Networks
Convolutional kernels are basic and vital components of deep Convolutional
Neural Networks (CNN). In this paper, we equip convolutional kernels with shape
attributes to generate the deep Irregular Convolutional Neural Networks (ICNN).
Compared to traditional CNN applying regular convolutional kernels like
, our approach trains irregular kernel shapes to better fit the
geometric variations of input features. In other words, shapes are learnable
parameters in addition to weights. The kernel shapes and weights are learned
simultaneously during end-to-end training with the standard back-propagation
algorithm. Experiments for semantic segmentation are implemented to validate
the effectiveness of our proposed ICNN.Comment: 7 pages, 5 figures, 3 table
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