Authors:Haruki Fujii andKazuhiro Hotta
Affiliation:Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya 468-8502, Japan
Keyword(s):Adaptive Resolution Selection, Small Objects, Semantic Segmentation, Cell Images, Medical Images.
Abstract:This paper proposes a segmentation method using adaptive resolution selection for improving the accuracy of small objects. In semantic segmentation, the segmentation of small objects is more difficult than that of large objects. Semantic segmentation requires both spatial details to locate objects and strong semantics to classify objects well, which are likely to exist at different resolution/scale levels. We believe that small objects are well represented by high-resolution feature maps, while large objects are suitable for low-resolution feature maps with high semantic information, and propose a method to automatically select a resolution and assign it to each object in the HRNet with multi-resolution feature maps. We propose Adaptive Resolution Selection Module (ARSM), which selects the resolution for segmentation of each class. The proposed method considers the feature map of each resolution in the HRNet as an Expert Network, and a Gating Network selects adequate resolution for each class. We conducted experiments on Drosophila cell images and the Covid 19 dataset, and confirmed that the proposed method achieved higher accuracy than the conventional method.(More)
This paper proposes a segmentation method using adaptive resolution selection for improving the accuracy of small objects. In semantic segmentation, the segmentation of small objects is more difficult than that of large objects. Semantic segmentation requires both spatial details to locate objects and strong semantics to classify objects well, which are likely to exist at different resolution/scale levels. We believe that small objects are well represented by high-resolution feature maps, while large objects are suitable for low-resolution feature maps with high semantic information, and propose a method to automatically select a resolution and assign it to each object in the HRNet with multi-resolution feature maps. We propose Adaptive Resolution Selection Module (ARSM), which selects the resolution for segmentation of each class. The proposed method considers the feature map of each resolution in the HRNet as an Expert Network, and a Gating Network selects adequate resolution for each class. We conducted experiments on Drosophila cell images and the Covid 19 dataset, and confirmed that the proposed method achieved higher accuracy than the conventional method.