Related papers: Class-Level Confidence Based 3D Semi-Supervised Learning

Class-Level Confidence Based 3D Semi-Supervised Learning

URL: http://arxiv.org/abs/2210.10138v1
Date: Tue, 18 Oct 2022 20:13:28 GMT
Title: Class-Level Confidence Based 3D Semi-Supervised Learning
Authors: Zhimin Chen, Longlong Jing, Liang Yang, Bing Li
Abstract summary: We show that unlabeled data class-level confidence can represent the learning status in the 3D imbalanced dataset. Our method significantly outperforms state-of-the-art counterparts for both 3D SSL classification and detection tasks.
Score: 18.95161296147023
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Recent state-of-the-art method FlexMatch firstly demonstrated that correctly estimating learning status is crucial for semi-supervised learning (SSL). However, the estimation method proposed by FlexMatch does not take into account imbalanced data, which is the common case for 3D semi-supervised learning. To address this problem, we practically demonstrate that unlabeled data class-level confidence can represent the learning status in the 3D imbalanced dataset. Based on this finding, we present a novel class-level confidence based 3D SSL method. Firstly, a dynamic thresholding strategy is proposed to utilize more unlabeled data, especially for low learning status classes. Then, a re-sampling strategy is designed to avoid biasing toward high learning status classes, which dynamically changes the sampling probability of each class. To show the effectiveness of our method in 3D SSL tasks, we conduct extensive experiments on 3D SSL classification and detection tasks. Our method significantly outperforms state-of-the-art counterparts for both 3D SSL classification and detection tasks in all datasets.

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