Incremental Learning with Maximum Entropy Regularization: Rethinking Forgetting and Intransigence. - Dahyun Kim 001; Jihwan Bae; Yeonsik Jo; Jonghyun Choi (CoRR, 2019)

Confidence Calibration for Incremental Learning. - Dongmin Kang; Yeonsik Jo; Yeongwoo Nam; Jonghyun Choi (IEEE Access, 2020)

Rethinking Deep Image Prior for Denoising. - Yeonsik Jo; Se Young Chun; Jonghyun Choi (ICCV, 2021)

CEDe: A collection of expert-curated datasets with atom-level entity annotations for Optical Chemical Structure Recognition. - Rodrigo Hormazabal; Changyoung Park; Soonyoung Lee; Sehui Han; Yeonsik Jo; Jaewan Lee; Ahra Jo; Seung Hwan Kim; Jaegul Choo; Moontae Lee; Honglak Lee (NeurIPS, 2022)

Misalign, Contrast then Distill: Rethinking Misalignments in Language-Image Pretraining. - Bumsoo Kim; Yeonsik Jo; Jinhyung Kim; Seung-Hwan Kim (ICCV, 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)

Expediting Contrastive Language-Image Pretraining via Self-Distilled Encoders. - Bumsoo Kim; Jinhyung Kim; Yeonsik Jo; Seung Hwan Kim (AAAI, 2024)

Single-stage table structure recognition approach via efficient sequence modeling. - Eunji Lee; Yeonsik Jo; Soonyoung Lee; Seung Hwan Kim; Nam Ik Cho (Pattern Recognit. Lett., 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)