The paper introduces the CMCC-ReID task, constructs the SYSU-CMCC benchmark dataset, and proposes the PIA network with disentangling and prototype modules that outperforms prior methods on combined modality and clothing variations.
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XAttnRes introduces cross-stage attention residuals that maintain a global feature history and selectively aggregate prior representations, improving medical image segmentation and performing on par with baselines even without skip connections.
Keypoint detection plus graph grouping produces per-cell bounding boxes for instance segmentation, outperforming prior methods on two cell datasets.
cGAN data augmentation with feature-based filtering improves ResNet18 CIN grading accuracy from 66.3% to 71.7% on segmented epithelium patches.
citing papers explorer
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CMCC-ReID: Cross-Modality Clothing-Change Person Re-Identification
The paper introduces the CMCC-ReID task, constructs the SYSU-CMCC benchmark dataset, and proposes the PIA network with disentangling and prototype modules that outperforms prior methods on combined modality and clothing variations.
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XAttnRes: Cross-Stage Attention Residuals for Medical Image Segmentation
XAttnRes introduces cross-stage attention residuals that maintain a global feature history and selectively aggregate prior representations, improving medical image segmentation and performing on par with baselines even without skip connections.
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Multi-scale Cell Instance Segmentation with Keypoint Graph based Bounding Boxes
Keypoint detection plus graph grouping produces per-cell bounding boxes for instance segmentation, outperforming prior methods on two cell datasets.
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Synthetic Augmentation and Feature-based Filtering for Improved Cervical Histopathology Image Classification
cGAN data augmentation with feature-based filtering improves ResNet18 CIN grading accuracy from 66.3% to 71.7% on segmented epithelium patches.