25 citations · 46 across the 9 of their papers we have counts for
10 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…
Learning to Learn Parameterized Classification Networks for Scalable Input Images
Duo Li, Anbang Yao, Qifeng Chen
Convolutional Neural Networks (CNNs) do not have a predictable recognition behavior with respect to the input resolution change. This prevents the feasibility of deployment on diff…
Resolution Switchable Networks for Runtime Efficient Image Recognition
Yikai Wang, Fuchun Sun, Duo Li +1
We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference. Thus the running speed can be selected to m…
Deep Reinforced Attention Learning for Quality-Aware Visual Recognition
Duo Li, Qifeng Chen
In this paper, we build upon the weakly-supervised generation mechanism of intermediate attention maps in any convolutional neural networks and disclose the effectiveness of attent…
Dynamic Hierarchical Mimicking Towards Consistent Optimization Objectives
Duo Li, Qifeng Chen
While the depth of modern Convolutional Neural Networks (CNNs) surpasses that of the pioneering networks with a significant margin, the traditional way of appending supervision onl…