240 citations · 314 across the 6 of their papers we have counts for
6 papers
Lift3D: Synthesize 3D Training Data by Lifting 2D GAN to 3D Generative Radiance Field
Leheng Li, Qing Lian, Luozhou Wang +2
This work explores the use of 3D generative models to synthesize training data for 3D vision tasks. The key requirements of the generative models are that the generated data should…
RepVGG: Making VGG-style ConvNets Great Again
Xiaohan Ding, Xiangyu Zhang, Ningning Ma +3
We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU,…
Activate or Not: Learning Customized Activation
Ningning Ma, Xiangyu Zhang, Ming Liu +1
We present a simple, effective, and general activation function we term ACON which learns to activate the neurons or not. Interestingly, we find Swish, the recent popular NAS-searc…
Funnel Activation for Visual Recognition
Ningning Ma, Xiangyu Zhang, Jian Sun
We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by ad…
WeightNet: Revisiting the Design Space of Weight Networks
Ningning Ma, Xiangyu Zhang, Jiawei Huang +1
We present a conceptually simple, flexible and effective framework for weight generating networks. Our approach is general that unifies two current distinct and extremely effective…
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng +1
Currently, the neural network architecture design is mostly guided by the \emph{indirect} metric of computation complexity, i.e., FLOPs. However, the \emph{direct} metric, e.g., sp…