25 citations · 41 across the 6 of their papers we have counts for
8 papers
Unifying Nonlocal Blocks for Neural Networks
Lei Zhu, Qi She, Duo Li +4
The nonlocal-based blocks are designed for capturing long-range spatial-temporal dependencies in computer vision tasks. Although having shown excellent performance, they still lack…
m-RevNet: Deep Reversible Neural Networks with Momentum
Duo Li, Shang-Hua Gao
In recent years, the connections between deep residual networks and first-order Ordinary Differential Equations (ODEs) have been disclosed. In this work, we further bridge the deep…
Learning the Superpixel in a Non-iterative and Lifelong Manner
Lei Zhu, Qi She, Bin Zhang +4
Superpixel is generated by automatically clustering pixels in an image into hundreds of compact partitions, which is widely used to perceive the object contours for its excellent c…
Involution: Inverting the Inherence of Convolution for Visual Recognition
Duo Li, Jie Hu, Changhu Wang +5
Convolution has been the core ingredient of modern neural networks, triggering the surge of deep learning in vision. In this work, we rethink the inherent principles of standard co…
PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation
Xiangtai Li, Hao He, Xia Li +6
Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general semantic segmentation does not have. There are two…
PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer
Duo Li, Anbang Yao, Qifeng Chen
Despite their strong modeling capacities, Convolutional Neural Networks (CNNs) are often scale-sensitive. For enhancing the robustness of CNNs to scale variance, multi-scale featur…