Search Results for author: Zheng-Ning Liu

Found 6 papers, 4 papers with code

Subdivision-Based Mesh Convolution Networks

1 code implementation4 Jun 2021 Shi-Min Hu, Zheng-Ning Liu, Meng-Hao Guo, Jun-Xiong Cai, Jiahui Huang, Tai-Jiang Mu, Ralph R. Martin

Meshes with arbitrary connectivity can be remeshed to hold Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach.

3D Classification

Can Attention Enable MLPs To Catch Up With CNNs?

no code implementations31 May 2021 Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, Dun Liang, Ralph R. Martin, Shi-Min Hu

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] on arXiv. org almost at the same time, each proposing new learning architectures, consisting mainly of linear layers, claiming them to be comparable, or even superior to convolutional-based models.

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

5 code implementations5 May 2021 Meng-Hao Guo, Zheng-Ning Liu, Tai-Jiang Mu, Shi-Min Hu

Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks.

Image Classification Image Generation +4

PCT: Point cloud transformer

8 code implementations17 Dec 2020 Meng-Hao Guo, Jun-Xiong Cai, Zheng-Ning Liu, Tai-Jiang Mu, Ralph R. Martin, Shi-Min Hu

It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.

Ranked #11 on 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric)

3D Part Segmentation 3D Point Cloud Classification

Learning to Reconstruct High-quality 3D Shapes with Cascaded Fully Convolutional Networks

no code implementations ECCV 2018 Yan-Pei Cao, Zheng-Ning Liu, Zheng-Fei Kuang, Leif Kobbelt, Shi-Min Hu

We present a data-driven approach to reconstructing high-resolution and detailed volumetric representations of 3D shapes.

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