Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation
arxiv(2024)
摘要
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
boundaries between different class-wise Gaussian mixtures. Based on the label
distributions, we leverage the GMM to generate high-quality pseudo labels for
more reliable supervision. Our framework is capable of solving different forms
of weak labels: image-level labels, points, scribbles, blocks, and
bounding-boxes. Extensive experiments on PASCAL, COCO, Cityscapes, and ADE20K
datasets demonstrate that our framework can effectively provide more reliable
supervision and outperform the state-of-the-art methods under all settings.
Code will be available at https://github.com/Luffy03/AGMM-SASS.
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