yujin2002
Hanyang University, Integrated Master's and Ph.D. Program, 2nd Cohort
yujin2002 Email
yujin2002 Email
yujin2002 LinkedIn
yujin2002 LinkedIn
PAKDD2026_paper_page
PAKDD2026_paper_page
Pairwise comparison labeling is emerging as it yields higher inter-rater reliability than conventional classification labeling, but exhaustive comparisons require quadratic cost. We propose Dodgersort, which leverages CLIP-based hierarchical pre-ordering, a neural ranking head and probabilistic ensemble (Elo, BTL, GP), epistemic--aleatoric uncertainty decomposition, and information-theoretic pair selection. It reduces human comparisons while improving the reliability of the rankings. In visual ranking tasks in medical imaging, historical dating, and aesthetics, Dodgersort achieves a 11--16\% annotation reduction while improving inter-rater reliability. Cross-domain ablations across four datasets show that neural adaptation and ensemble uncertainty are key to this gain. In FG-NET with ground-truth ages, the framework extracts 5--20$\times$ more ranking information per comparison than baselines, yielding Pareto-optimal accuracy--efficiency trade-offs.
CIKM2025_paper_page
CIKM2025_paper_page
Y Park, H Chung, I Jang, Proceedings of the 34th ACM International Conference on Information and …, 2025 - 인용 횟수 1번
EM& Intelligent Design(Hanyang.Univ. lab)
EM& Intelligent Design(Hanyang.Univ. lab)
EM& Intelligent Design(Hanyang.Univ. lab)
LAB HAI (Hankuk Univ. lab)
LAB HAI (Hankuk Univ. lab)
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