Can I read Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation on EtoBox?

Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation by Wu, Linshan; Zhong, Zhun; Ma, Jiayi; Wei, Yunchao; Chen, Hao; Fang, Leyuan; Li, Shutao is a scholarly article available to read on EtoBox.

What is Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation about?

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models by weak labels, which is receiving significant attention due to its low annotation cost. Existing approaches focus on generating pseudo labels for supervision while largely ignoring to leverage the inherent semantic correlation among different pseudo labels. We observe that pseudo-labeled pixels that are close to each other in the feature space are more likely to share the same class, and those closer to the distribution centers tend to have higher confidence. Motivated by this, we propose to model the underlying label distributions and employ cross-label constraints to generate more accurate pseudo labels. In this paper, we develop a unified WSSS framework named Adaptive Gaussian Mixtures Model, which leverages a GMM to model the label distributions. Specifically, we calculate the feature distribution centers of pseudo-labeled pixels and build the GMM by measuring the distance between the centers and each pseudo-labeled pixel. Then, we introduce an Online Expectation-Maximization (OEM) algorithm and a novel maximization loss to optimize the GMM adaptively, aiming to learn more discriminative decision

Author

Wu, Linshan; Zhong, Zhun; Ma, Jiayi; Wei, Yunchao; Chen, Hao; Fang, Leyuan; Li, Shutao

Published

2024

Language

EN