MemBench: Memorized Image Trigger Prompt Dataset for Diffusion Models
Year: 2025 / Author: Chunsan Hong, Tae-Hyun Oh, Minhyuk Sung / Journal: TMLR
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Physical AI and Robotic Foundation Models
Physical AI and Robotic Foundation Models
The Lab drives a paradigm shift toward embodied intelligence by developing Robotic Foundation Models. By integrating vision, language, and multi-modal sensory data, these models empower robots to navigate unpredictable environments and perform contextual reasoning, all while ensuring the highest standards of safety and system trustworthiness
Publications List
VLM’s Eye Examination: Instruct and Inspect Visual Competency of Vision Language Models
Year: 2025 / Author : Nam Hyeon-Woo, Moon Ye-Bin, Wonseok Choi, Lee Hyun, Tae-Hyun Oh / Journal : TMLR
Enhancing Cost Efficiency in Active Learning with Candidate Set Query
Year: 2025 / Author: Yeho Gwon, Sehyun Hwang, Hoyoung Kim, Jungseul Ok, Suha Kwak / Journal: TMLR
A Unified Framework for Motion Reasoning and Generation in Human Interaction
Year: 2025 / Author: Jeongeun Park, Sungjoon Choi, Sangdoo Yun / Conference: International Conference on Computer Vision (ICCV)
Quantized Factor Identifiable Causal Effect Variational Autoencoder
Year: 2025 / Author: Sujeong Song, Junghyo Sohn, Eunsong Kang, Heung-Il Suk / Conference: ACM International Conference on Information and Knowledge Management (CIKM)
Frequency-Conditioned Diffusion Models for Time Series Generation
Year: 2025 / Author: Seungwoo Jeong, Junghyo Sohn, Jaehyun Jeon, Yeajin Shon, Heung-Il Suk / Conference: ACM International Conference on Information and Knowledge Management (CIKM)
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