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20162025
most citedUniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning

108 citations · 791 across the 43 of their papers we have counts for

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Showing 2021Show all

8 papers · 1 filter

cs.CV2021★ 11 cited

Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

Liang Pan, Tong Wu, Zhongang Cai +26

As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem f…

cs.CV2021★ 3 cited

Self-slimmed Vision Transformer

Zhuofan Zong, Kunchang Li, Guanglu Song +4

Vision transformers (ViTs) have become the popular structures and outperformed convolutional neural networks (CNNs) on various vision tasks. However, such powerful transformers bri…

cs.CV2021★ 5 cited

MorphMLP: An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning

David Junhao Zhang, Kunchang Li, Yali Wang +5

Recently, MLP-Like networks have been revived for image recognition. However, whether it is possible to build a generic MLP-Like architecture on video domain has not been explored,…

cs.CV2021★ 40 cited

Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos

Junhao Zhang, Yali Wang, Zhipeng Zhou +3

Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. However, it is often built on the fixed human-joint affinity, according to human…

cs.CV2021★ 2 cited

Digging into Uncertainty in Self-supervised Multi-view Stereo

Hongbin Xu, Zhipeng Zhou, Yali Wang +4

Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions,…

cs.CV2021★ 25 cited

CT-Net: Channel Tensorization Network for Video Classification

Kunchang Li, Xianhang Li, Yali Wang +2

3D convolution is powerful for video classification but often computationally expensive, recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions…