Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation

• URL: http://arxiv.org/abs/2310.09760v3
• Date: Tue, 26 Nov 2024 02:11:53 GMT
• Title: Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation
• Authors: Wangyu Wu, Tianhong Dai, Xiaowei Huang, Fei Ma, Jimin Xiao,
• Abstract summary: We introduce a novel approach called Image Augmentation with Controlled Diffusion (IACD) IACD effectively augments existing labeled datasets by generating diverse images through controlled diffusion. We also propose a high-quality image selection strategy to mitigate the potential noise introduced by the randomness of diffusion models.
• Score: 23.888222298960542
• License:
• 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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