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arXiv · 2310.09760

Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation

Wangyu Wu·Tianhong Dai·Xiaowei Huang·Fei Ma·Jimin Xiao
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Abstract

Weakly-supervised semantic segmentation (WSSS), which aims to train segmentation models solely using image-level labels, has achieved significant attention. Existing methods primarily focus on generating high-quality pseudo labels using available images and their image-level labels. However, the quality of pseudo labels degrades significantly when the size of available dataset is limited. Thus, in this paper, we tackle this problem from a different view by introducing a novel approach called Image Augmentation with Controlled Diffusion (IACD). This framework effectively augments existing labeled datasets by generating diverse images through controlled diffusion, where the available images and image-level labels are served as the controlling information. Moreover, we also propose a high-quality image selection strategy to mitigate the potential noise introduced by the randomness of diffusion models. In the experiments, our proposed IACD approach clearly surpasses existing state-of-the-art methods. This effect is more obvious when the amount of available data is small, demonstrating the effectiveness of our method.

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Wangyu Wu, Tianhong Dai, Xiaowei Huang, Fei Ma, Jimin Xiao. 2023-10-15. Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation. https://arxiv.org/abs/2310.09760

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