32 citations · 51 across the 5 of their papers we have counts for
5 papers
Towards Lightweight Super-Resolution with Dual Regression Learning
Yong Guo, Mingkui Tan, Zeshuai Deng +5
Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks by learning a mapping from low-resolution (LR) images to high-resolution (HR) images…
Results of the NeurIPS'21 Challenge on Billion-Scale Approximate Nearest Neighbor Search
Harsha Vardhan Simhadri, George Williams, Martin Aumüller +9
Despite the broad range of algorithms for Approximate Nearest Neighbor Search, most empirical evaluations of algorithms have focused on smaller datasets, typically of 1 million poi…
SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search
Qi Chen, Bing Zhao, Haidong Wang +5
The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved great success for fast high-recall search, but are extremely expensive when handling very larg…
Content-Aware Convolutional Neural Networks
Yong Guo, Yaofo Chen, Mingkui Tan +3
Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverse…
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Yong Guo, Jian Chen, Jingdong Wang +5
Deep neural networks have exhibited promising performance in image super-resolution (SR) by learning a nonlinear mapping function from low-resolution (LR) images to high-resolution…