Clustering as Attention: Unified Image Segmentation with Hierarchical Clustering

• URL: http://arxiv.org/abs/2205.09949v1
• Date: Fri, 20 May 2022 03:53:56 GMT
• Title: Clustering as Attention: Unified Image Segmentation with Hierarchical Clustering
• Authors: Teppei Suzuki
• Abstract summary: We propose a hierarchical clustering-based image segmentation scheme for deep neural networks, called HCFormer. We interpret image segmentation, including semantic, instance, and panoptic segmentation, as a pixel clustering problem, and accomplish it by bottom-up, hierarchical clustering with deep neural networks.
• Score: 11.696069523681178
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a hierarchical clustering-based image segmentation scheme for deep neural networks, called HCFormer. We interpret image segmentation, including semantic, instance, and panoptic segmentation, as a pixel clustering problem, and accomplish it by bottom-up, hierarchical clustering with deep neural networks. Our hierarchical clustering removes the pixel decoder from conventional segmentation models and simplifies the segmentation pipeline, resulting in improved segmentation accuracies and interpretability. HCFormer can address semantic, instance, and panoptic segmentation with the same architecture because the pixel clustering is a common approach for various image segmentation. In experiments, HCFormer achieves comparable or superior segmentation accuracies compared to baseline methods on semantic segmentation (55.5 mIoU on ADE20K), instance segmentation (47.1 AP on COCO), and panoptic segmentation (55.7 PQ on COCO).

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