A Unified Approach to Interpreting Self-supervised Pre-training Methods for 3D Point Clouds via Interactions
Published in CVPR, 2025
Recommended citation: Li, Q., Ruan, J., Wu, F., Chen, Y., Wei, Z., & Shen, W. A Unified Approach to Interpreting Self-supervised Pre-training Methods for 3D Point Clouds via Interactions. In CVPR 2025. https://openaccess.thecvf.com/content/CVPR2025/papers/Li_A_Unified_Approach_to_Interpreting_Self-supervised_Pre-training_Methods_for_3D_CVPR_2025_paper.pdf
Abstract. This paper uses game-theoretic interactions as a unified approach to interpret self-supervised pre-training methods for 3D point clouds and identifies a shared mechanism behind their performance gains.
Authors: Qiang Li, Jian Ruan, Fanghao Wu, Yuchi Chen, Zhihua Wei†, Wen Shen†.
Recommended citation: Li, Q., Ruan, J., Wu, F., Chen, Y., Wei, Z., & Shen, W. A Unified Approach to Interpreting Self-supervised Pre-training Methods for 3D Point Clouds via Interactions. In CVPR 2025.