167 citations · 201 across the 4 of their papers we have counts for
4 papers
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…
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…
Generating Multi-Sentence Lingual Descriptions of Indoor Scenes
Dahua Lin, Chen Kong, Sanja Fidler +1
This paper proposes a novel framework for generating lingual descriptions of indoor scenes. Whereas substantial efforts have been made to tackle this problem, previous approaches f…