1 code implementation • 28 Mar 2023 • Wenjie Wang, Xinyu Lin, Liuhui Wang, Fuli Feng, Yunshan Ma, Tat-Seng Chua
Inspired by the causal graph, our key considerations to handle preference shifts lie in modeling the interaction generation procedure by: 1) capturing the preference shifts across environments for accurate preference prediction, and 2) disentangling the sparse influence from user preference to interactions for accurate effect estimation of preference.
2 code implementations • 14 Mar 2023 • Renrui Zhang, Liuhui Wang, Ziyu Guo, Yali Wang, Peng Gao, Hongsheng Li, Jianbo Shi
We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions.
Ranked #1 on Training-free 3D Part Segmentation on ShapeNet-Part
3D Point Cloud Classification Training-free 3D Part Segmentation +1
no code implementations • 1 Mar 2023 • Renrui Zhang, Liuhui Wang, Ziyu Guo, Jianbo Shi
Performances on standard 3D point cloud benchmarks have plateaued, resulting in oversized models and complex network design to make a fractional improvement.
1 code implementation • CVPR 2023 • Renrui Zhang, Liuhui Wang, Yali Wang, Peng Gao, Hongsheng Li, Jianbo Shi
We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions.
2 code implementations • CVPR 2023 • Renrui Zhang, Liuhui Wang, Yu Qiao, Peng Gao, Hongsheng Li
Pre-training by numerous image data has become de-facto for robust 2D representations.
Ranked #2 on 3D Point Cloud Linear Classification on ModelNet40 (using extra training data)
3D Point Cloud Linear Classification Few-Shot 3D Point Cloud Classification