1 citations · 1 across the 6 of their papers we have counts for
6 papers
HuRo: Robotizing Human Videos for Scalable VLA Pretraining
Jinho Jeong, Se June Joo, Jaehyun Kang +4
Human video datasets offer an abundant and diverse source of interaction data that can complement expensive real-robot data. To bridge the human-to-robot embodiment gap, existing a…
Hierarchical Latent Action Model
Hanjung Kim, Lerrel Pinto, Seon Joo Kim
Latent Action Models (LAMs) enable learning from actionless data for applications ranging from robotic control to interactive world models. However, existing LAMs typically focus o…
A Training-Free, Task-Agnostic Framework for Enhancing MLLM Performance on High-Resolution Images
Jaeseong Lee, Yeeun Choi, Heechan Choi +2
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in vision-language understanding, reasoning, and generation. However, they struggle with tasks re…
UniSkill: Imitating Human Videos via Cross-Embodiment Skill Representations
Hanjung Kim, Jaehyun Kang, Hyolim Kang +3
Mimicry is a fundamental learning mechanism in humans, enabling individuals to learn new tasks by observing and imitating experts. However, applying this ability to robots presents…
VISAGE: Video Instance Segmentation with Appearance-Guided Enhancement
Hanjung Kim, Jaehyun Kang, Miran Heo +3
In recent years, online Video Instance Segmentation (VIS) methods have shown remarkable advancement with their powerful query-based detectors. Utilizing the output queries of the d…
Soft-Landing Strategy for Alleviating the Task Discrepancy Problem in Temporal Action Localization Tasks
Hyolim Kang, Hanjung Kim, Joungbin An +2
Temporal Action Localization (TAL) methods typically operate on top of feature sequences from a frozen snippet encoder that is pretrained with the Trimmed Action Classification (TA…