2 citations · 2 across the 14 of their papers we have counts for
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Decoupled MeanFlow: Turning Flow Models into Flow Maps for Accelerated Sampling
Kyungmin Lee, Sihyun Yu, Jinwoo Shin
Denoising generative models, such as diffusion and flow-based models, produce high-quality samples but require many denoising steps due to discretization error. Flow maps, which es…
Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model
John Won, Kyungmin Lee, Huiwon Jang +2
Augmenting vision-language-action models (VLAs) with world models is promising for robotic policy learning but faces challenges in jointly predicting states and actions due to the…
Contrastive Representation Regularization for Vision-Language-Action Models
Taeyoung Kim, Jimin Lee, Myungkyu Koo +5
Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained Vision-Language Models (VLMs). However,…
HAMLET: Switch your Vision-Language-Action Model into a History-Aware Policy
Myungkyu Koo, Daewon Choi, Taeyoung Kim +4
Inherently, robotic manipulation tasks are history-dependent: leveraging past context could be beneficial. However, most existing Vision-Language-Action models (VLAs) have been des…
StarFT: Robust Fine-tuning of Zero-shot Models via Spuriosity Alignment
Younghyun Kim, Jongheon Jeong, Sangkyung Kwak +3
Learning robust representations from data often requires scale, which has led to the success of recent zero-shot models such as CLIP. However, the obtained robustness can easily be…
Improving Motion in Image-to-Video Models via Adaptive Low-Pass Guidance
June Suk Choi, Kyungmin Lee, Sihyun Yu +3
Recent text-to-video (T2V) models have demonstrated strong capabilities in producing high-quality, dynamic videos. To improve the visual controllability, recent works have consider…
Calibrated Multi-Preference Optimization for Aligning Diffusion Models
Kyungmin Lee, Xiaohang Li, Qifei Wang +7
Aligning text-to-image (T2I) diffusion models with preference optimization is valuable for human-annotated datasets, but the heavy cost of manual data collection limits scalability…