activity
20172022
most citedPerformance Guaranteed Network Acceleration via High-Order Residual Quantization

19 citations · 26 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20223 cited

Boosting Point Clouds Rendering via Radiance Mapping

Xiaoyang Huang, Yi Zhang, Bingbing Ni +3

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-b…

cs.CV20223 cited

Representation-Agnostic Shape Fields

Xiaoyang Huang, Jiancheng Yang, Yanjun Wang +5

3D shape analysis has been widely explored in the era of deep learning. Numerous models have been developed for various 3D data representation formats, e.g., MeshCNN for meshes, Po…

cs.CV20211 cited

Progressive Stage-wise Learning for Unsupervised Feature Representation Enhancement

Zefan Li, Chenxi Liu, Alan Yuille +3

Unsupervised learning methods have recently shown their competitiveness against supervised training. Typically, these methods use a single objective to train the entire network. Bu…

cs.CV2021

3D Human Action Representation Learning via Cross-View Consistency Pursuit

Linguo Li, Minsi Wang, Bingbing Ni +3

In this work, we propose a Cross-view Contrastive Learning framework for unsupervised 3D skeleton-based action Representation (CrosSCLR), by leveraging multi-view complementary sup…

cs.CV201719 cited

Performance Guaranteed Network Acceleration via High-Order Residual Quantization

Zefan Li, Bingbing Ni, Wenjun Zhang +2

Input binarization has shown to be an effective way for network acceleration. However, previous binarization scheme could be regarded as simple pixel-wise thresholding operations (…