167 citations · 235 across the 12 of their papers we have counts for
5 papers · 1 filter
CNNs are Globally Optimal Given Multi-Layer Support
Chen Huang, Chen Kong, Simon Lucey
Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs. Although giving impressive empirical performance it can be slow to converge. In this paper we e…
Take it in your stride: Do we need striding in CNNs?
Chen Kong, Simon Lucey
Since their inception, CNNs have utilized some type of striding operator to reduce the overlap of receptive fields and spatial dimensions. Although having clear heuristic motivatio…
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
Jhony K. Pontes, Chen Kong, Sridha Sridharan +3
One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks. Recent works have relied on volumetric or point cloud representat…
Compact Model Representation for 3D Reconstruction
Jhony K. Pontes, Chen Kong, Anders Eriksson +3
3D reconstruction from 2D images is a central problem in computer vision. Recent works have been focusing on reconstruction directly from a single image. It is well known however t…
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
Chen-Hsuan Lin, Chen Kong, Simon Lucey
Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D…