2D and 3D visual understanding with limited supervision

Abstract

under various perturbations to effectively regularize unlabeled 3D points. In particular, we propose a novel Dual Adaptive Transformations (DAT) model for weakly supervised point cloud segmentation, where the dual adaptive transformations are performed via an adversarial strategy at both point-level and region-level, aiming at enforcing the local and structural smoothness constraints on 3D point clouds. Secondly, in Chapter 6, we observe that the straightforward way of applying consistency constraints to weakly supervised point cloud segmentation has two major limitations: unsatisfied pseudo labels due to the conventional confidence-based selection and insufficient consistency constraints due to discarding unreliable pseudo labels. Therefore, we propose a novel Reliability-Adaptive Consistency Network (RAC-Net) to use both prediction confidence and model uncertainty to measure the reliability of pseudo labels and apply consistency training on all unlabeled points while with different consistency constraints for different points based on the reliability of corresponding pseudo labels.