Research Groups

We explore the intersection of computational perception and efficient learning algorithms to advance intelligent systems. Our research optimizes how artificial agents acquire, represent, and adapt knowledge with minimal computational overhead. We try to trailblaze scalable, sustainable intelligence that works in real-world environments. Please refer to "Join Us" page in our site.

Computer Vision
Machine Learning
Multimodal AI
Embodied AI
Continual Learning
Binary Networks
Neuromorphic (Event) Cameras
Natural Language Processing

- Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts, Taewook Kang*, Taeheon Kim*, Donghyun Shin, Jonghyun Choi, ECCV 2026
- SCALE: Self-uncertainty Conditioned Adaptive Looking and Execution for Vision-Language-Action Models, Hyeonbeom Choi, Daechul Ahn, Youhan Lee, Taewook Kang, Seongwon Cho, Jonghyun Choi, ICML 2026 (Spotlight, Top 2.2%)
- Budgeted Online Continual Learning by Adaptive Layer Freezing and Frequency-based Sampling, Minhyuk Seo, Hyunseo Koh, Jonghyun Choi, ICLR 2025 - Spotlight
- Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback, Daechul Ahn, Yura Choi, Youngjae Yu, Dongyeop Kang, Jonghyun Choi, ACL 2024 - Oral
- Pre-emptive Action Revision by Environmental Feedback for Embodied Instruction Following Agents, Jinyeon Kim*, Cheolhong Min*, Byeonghwi Kim, Jonghyun Choi, CoRL 2024
- Context-Aware Planning and Environment-Aware Memory for Instruction Following Embodied Agents, Byeonghwi Kim, Jinyeon Kim, Yuyeong Kim, Cheolhong Min, Jonghyun Choi, ICCV 2023
- Ask4Help: Learning to Leverage an Expert for Embodied Tasks, Kunal Pratap Singh, Luca Weihs, Alvaro Herrasti, Jonghyun Choi, Aniruddha Kembhavi, Roozbeh Mottaghi, NeurIPS 2022