Image Augmentation with Controlled Diffusion for Weakly-Supervised Semantic Segmentation
This addresses a bottleneck in weakly-supervised semantic segmentation for computer vision applications, offering an incremental improvement by augmenting data to enhance pseudo label quality.
The paper tackles the problem of limited dataset size degrading pseudo label quality in weakly-supervised semantic segmentation by introducing Image Augmentation with Controlled Diffusion (IACD) to generate diverse images, resulting in clear performance improvements over state-of-the-art methods, especially with small datasets.
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.