activity
20202022
most citedBeyond Self-attention: External Attention using Two Linear Layers for Visual Tasks

83 citations · 110 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV20225 cited

MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors

Zi-Xin Zou, Shi-Sheng Huang, Yan-Pei Cao +3

High-fidelity 3D scene reconstruction from monocular videos continues to be challenging, especially for complete and fine-grained geometry reconstruction. The previous 3D reconstru…

cs.CV20212 cited

Can Attention Enable MLPs To Catch Up With CNNs?

Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu +3

In the first week of May, 2021, researchers from four different institutions: Google, Tsinghua University, Oxford University and Facebook, shared their latest work [16, 7, 12, 17]…

cs.CV202183 cited

Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks

Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu +1

Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks. Self-attention updates the feature at e…

cs.CV20213 cited

Recursive-NeRF: An Efficient and Dynamically Growing NeRF

Guo-Wei Yang, Wen-Yang Zhou, Hao-Yang Peng +3

View synthesis methods using implicit continuous shape representations learned from a set of images, such as the Neural Radiance Field (NeRF) method, have gained increasing attenti…

cs.CV2020

PCT: Point cloud transformer

Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu +3

The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Tra…

cs.CV20202 cited

Alternating ConvLSTM: Learning Force Propagation with Alternate State Updates

Congyue Deng, Tai-Jiang Mu, Shi-Min Hu

Data-driven simulation is an important step-forward in computational physics when traditional numerical methods meet their limits. Learning-based simulators have been widely studie…