연구 - National AI Research Lab

#Breakthrough in Neural Scaling Law

Active Prompt Learning with Vision-Language Model Priors

연도: 2025 / 저자: 김호영, 진석희, 성창환, 김재창, 옥정슬 / 학술지: TMLR

#Foundation Model for Robotics

MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models

연도: 2025 / 저자: 홍춘산, 오태현, 성민혁 / 학술지: TMLR

#Foundation Model for Robotics

VLM’s Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models

연도: 2025 / 저자: 남현우, 문예빈, 최원석, 이현, 오태현 / 학술지: TMLR

#Foundation Model for Robotics

Enhancing Cost Efficiency in Active Learning with Candidate Set Query

연도: 2025 / 저자: 권예호, 황세현, 김호영, 옥정슬, 곽수하 / 학술지: TMLR

#High-dimensional Generative Foundation Models

AM-Adapter: Appearance Matching Adapter for Exemplar-based Semantic Image Synthesis

연도: 2025 / 저자: 진시윤, 남지수, 김지영, 정다현, 김영석, 박준형, 추헌정, 김승룡 / 학술대회명: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

#High-dimensional Generative Foundation Models

Sparsely Labeled fMRI Data Denoising with Meta-Learning-Based Semi-Supervised Domain Adaptation

연도: 2025 / 저자: 허근수, 한지웅, 박소연, 임민주, 강보경, 박상준, Weili Lin, Han Zhang, Dinggang Shen, 감태의 / 학술대회명: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

Research Archive

중점 연구 분야

연구 논문

Active Prompt Learning with Vision-Language Model Priors

MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models

VLM’s Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models

Enhancing Cost Efficiency in Active Learning with Candidate Set Query

AM-Adapter: Appearance Matching Adapter for Exemplar-based Semantic Image Synthesis

Sparsely Labeled fMRI Data Denoising with Meta-Learning-Based Semi-Supervised Domain Adaptation

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