Enhancing Pseudo-Labeling Performance in Object Detection Using Gaussian Mixture Modeled Uncertainty
Seungil Lee, Hyun Kim · 2024
Object detection research has been rapidly advancing. However, it requires large amounts of training data, where labeling massive datasets incurs great cost and time. To address this problem, semi-supervised learning techniques have been increasingly explored, among which pseudo-labeling has become popular due to its straightforward approach. However, pseudo-labeling has limitations with confidence score-based filtering. In this paper, we propose a method to extract uncertainties using Gaussian mixture models and effectively incorporate them into the labeling process to overcome these limitations. The proposed method achieves more reliable pseudo-labeling results and experiments show a 0.8% performance improvement compared to the existing approach.