CVPR 2017

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

CVPR 2017 ElliotHYLee/VisualOdometry3D

Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.


Xception: Deep Learning with Depthwise Separable Convolutions

CVPR 2017 modelhub-ai/xception

We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).