Lim, Jongin

9 publications

ICML 2025 PRIME: Deep Imbalanced Regression with Proxies Jongin Lim, Sucheol Lee, Daeho Um, Sung-Un Park, Jinwoo Shin

ICML 2025 Propagate and Inject: Revisiting Propagation-Based Feature Imputation for Graphs with Partially Observed Features Daeho Um, Sunoh Kim, Jiwoong Park, Jongin Lim, Seong Jin Ahn, Seulki Park

ICLR 2025 Spreading Out-of-Distribution Detection on Graphs Daeho Um, Jongin Lim, Sunoh Kim, Yuneil Yeo, Yoonho Jung

CVPR 2023 BiasAdv: Bias-Adversarial Augmentation for Model Debiasing Jongin Lim, Youngdong Kim, Byungjai Kim, Chanho Ahn, Jinwoo Shin, Eunho Yang, Seungju Han

ICCV 2023 Sample-Wise Label Confidence Incorporation for Learning with Noisy Labels Chanho Ahn, Kikyung Kim, Ji-won Baek, Jongin Lim, Seungju Han

CVPR 2022 Hypergraph-Induced Semantic Tuplet Loss for Deep Metric Learning Jongin Lim, Sangdoo Yun, Seulki Park, Jin Young Choi

AAAI 2021 Class-Attentive Diffusion Network for Semi-Supervised Classification Jongin Lim, Daeho Um, Hyung Jin Chang, Dae Ung Jo, Jin Young Choi

ICCV 2021 Influence-Balanced Loss for Imbalanced Visual Classification Seulki Park, Jongin Lim, Younghan Jeon, Jin Young Choi

AAAI 2019 Backbone Cannot Be Trained at Once: Rolling Back to Pre-Trained Network for Person Re-Identification Youngmin Ro, Jongwon Choi, Dae Ung Jo, Byeongho Heo, Jongin Lim, Jin Young Choi