no code implementations • ICCV 2021 • Xing Nie, Yongcheng Liu, Shaohong Chen, Jianlong Chang, Chunlei Huo, Gaofeng Meng, Qi Tian, Weiming Hu, Chunhong Pan
It can work in a purely data-driven manner and thus is capable of auto-creating a group of suitable convolutions for geometric shape modeling.
1 code implementation • ICCV 2019 • Yongcheng Liu, Bin Fan, Gaofeng Meng, Jiwen Lu, Shiming Xiang, Chunhong Pan
Point cloud processing is very challenging, as the diverse shapes formed by irregular points are often indistinguishable.
Ranked #22 on 3D Part Segmentation on ShapeNet-Part
4 code implementations • CVPR 2019 • Yongcheng Liu, Bin Fan, Shiming Xiang, Chunhong Pan
Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others.
Ranked #10 on 3D Point Cloud Classification on ModelNet40-C
1 code implementation • 16 Sep 2018 • Yongcheng Liu, Lu Sheng, Jing Shao, Junjie Yan, Shiming Xiang, Chunhong Pan
Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs.
Ranked #9 on Multi-Label Classification on NUS-WIDE
1 code implementation • 30 Jul 2018 • Yongcheng Liu, Bin Fan, Lingfeng Wang, Jun Bai, Shiming Xiang, Chunhong Pan
Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation.