Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels. - Jiwon Kim; Kwangrok Ryoo; Junyoung Seo; Gyuseong Lee; Daehwan Kim; Hansang Cho; Seungryong Kim (CVPR, 2022)

ConMatch: Semi-supervised Learning with Confidence-Guided Consistency Regularization. - Jiwon Kim; Youngjo Min; Daehwan Kim; Gyuseong Lee; Junyoung Seo; Kwangrok Ryoo; Seungryong Kim (ECCV (30), 2022)

Towards Flexible Inductive Bias via Progressive Reparameterization Scheduling. - Yunsung Lee; Gyuseong Lee; Kwangrok Ryoo; Hyojun Go; Jihye Park; Seungryong Kim (ECCV Workshops (4), 2022)

AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning. - Jiwon Kim; Kwangrok Ryoo; Gyuseong Lee; Seokju Cho; Junyoung Seo; Daehwan Kim; Hansang Cho; Seungryong Kim (CoRR, 2022)

3D GAN Inversion with Pose Optimization. - Jaehoon Ko; Kyusun Cho; Daewon Choi; Kwangrok Ryoo; Seungryong Kim (WACV, 2023)

Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection. - Kwangrok Ryoo; Yeonsik Jo; Seungjun Lee; Mira Kim; Ahra Jo; Seung Hwan Kim; Seungryong Kim; Soonyoung Lee (CoRR, 2023)

SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels. - Daehwan Kim; Kwangrok Ryoo; Hansang Cho; Seungryong Kim (Int. J. Comput. Vis., 2025)

MolMole: Molecule Mining from Scientific Literature. - LG AI Research; Sehyun Chun; Jiye Kim; Ahra Jo; Yeonsik Jo; Seungyul Oh; Seungjun Lee; Kwangrok Ryoo; Jongmin Lee; Seung Hwan Kim; Byung Jun Kang; Soonyoung Lee; et al. (CoRR, 2025)

Correction: SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels. - Daehwan Kim; Kwangrok Ryoo; Hansang Cho; Seungryong Kim (Int. J. Comput. Vis., 2025)