CircleID introduces a controlled dataset of 46,155 circles from 66 writers and 8 pens, with competition results showing top accuracies of 64.8% for open-set writer identification and 92.7% for pen classification.
In: Proceedings of the IEEE/CVF international conference on computer vision
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3representative citing papers
OASIC uses anomaly-based masking and severity estimation to select occlusion-matched models, improving AUC on occluded images by up to 23.7 points.
Semi-MedRef introduces T-PatchMix, PosAug, and ITCL within a teacher-student SSL setup to preserve image-text alignment under augmentation for medical referring segmentation on QaTa-COV19 and MosMedData+.
citing papers explorer
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ICDAR 2026 Competition on Writer Identification and Pen Classification from Hand-Drawn Circles
CircleID introduces a controlled dataset of 46,155 circles from 66 writers and 8 pens, with competition results showing top accuracies of 64.8% for open-set writer identification and 92.7% for pen classification.
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OASIC: Occlusion-Agnostic and Severity-Informed Classification
OASIC uses anomaly-based masking and severity estimation to select occlusion-matched models, improving AUC on occluded images by up to 23.7 points.
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Semi-MedRef: Semi-Supervised Medical Referring Image Segmentation with Cross-Modal Alignment
Semi-MedRef introduces T-PatchMix, PosAug, and ITCL within a teacher-student SSL setup to preserve image-text alignment under augmentation for medical referring segmentation on QaTa-COV19 and MosMedData+.