Research Scientist, NAVER Cloud (Image Vision)
I'm a Research Scientist at NAVER Cloud and a Ph.D. student at KAIST. My current research focuses on self-improving agents — agents for agents that analyze execution trajectories, identify limitations, and iteratively improve agent systems with frozen model weights. I currently focus on visual agents, with the goal of developing methods that generalize across agent systems.
My background spans visual recognition — segmentation, detection, and matting — and grounded VLMs and visual reasoning agents, with 14+ papers at CVPR, ICCV, ECCV, NeurIPS, and AAAI (608 citations, h-index 10). I bring that experience in perception and diagnostic reasoning, along with a track record of taking research into products used by millions, to my current work on agents that improve through experience.
Agents for Agents
Developing meta-agents that analyze execution trajectories, identify where an agent falls short, propose and apply targeted changes, and re-evaluate its behavior. This improvement loop refines the agent harness — its prompts and instructions, workflows and tool use, code and architecture, and memory and skills — while keeping all model weights frozen.
Currently focused on visual agents, with the goal of developing improvement methods that generalize across agent systems.
Studied grounded VLMs that connect language to pixel- and region-level evidence, and visual reasoning agents that compose specialist vision models into executable workflows.
Failure diagnosis & targeted program repair
Promptable, zero-shot segmentation & matting for anything; label-efficient human matting.
ZIM (ICCV'25 Highlight) · WSSHM
Weakly- and semi-supervised semantic & instance segmentation from cheap supervision.
PointWSSIS · BESTIE · DRS · WSSS-BED
Class-incremental and panoptic continual segmentation; lightweight, efficient models.
ECLIPSE · SSUL · EResFD · PfLayer
Visual reasoning agents. A diagnostic framework for 3D spatial reasoning that turns silent perception failures in visual program synthesis into typed diagnoses, driving targeted program repair to rival frontier VLMs without task-specific training.
"Beyond AI, Into Agents." A researcher's story from the AI agent era.
event →
CLOVA-X Image Editing: the world of pixel magic delivered by AI.
session →
How to land an AI research engineer role — career mentoring (NAVER CLOVA).
SSUL: Semantic Segmentation with Unknown Label for Class-Incremental Learning.
social →Weakly-Supervised Instance Segmentation (CVPR 2022), hosted by Prof. Chaeeun Rhee.