Profil bibliographique

M. Koo

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9Publications signalées
30Citations signalées
0Affiliations récentes

Les domaines associés

Multimodal Machine Learning ApplicationsAdvanced Neural Network ApplicationsHuman Pose and Action RecognitionAction Observation and SynchronizationAsthma and respiratory diseases

Les publications récentes

Accès ouvert 2026 preprint OpenAlex

RLDX-1 Technical Report

Dongyoung Kim, Huiwon Jang, M. Koo, Suhyeok Jang et autres

While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion …

0 citations arXiv (Cornell University)
Accès ouvert 2026 preprint OpenAlex

Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models

Jimin Lee, Huiwon Jang, M. Koo, Jungwoo Park et autres

Humans understand and interact with the real world by relying on diverse physical feedback beyond visual perception. Motivated by this, recent approaches attempt to incorporate physical sensory signals into Vision-Language-Action models (VLAs). However, they typically focus on a single type of physical …

0 citations arXiv (Cornell University)
Accès ouvert 2025 preprint OpenAlex

Contrastive Representation Regularization for Vision-Language-Action Models

Tae‐Young Kim, Jimin Lee, M. Koo, Dongyoung Kim et autres

Vision-Language-Action (VLA) models have shown strong capabilities in robot manipulation by leveraging rich representations from pre-trained Vision-Language Models (VLMs). However, their representations arguably remain suboptimal, lacking sensitivity to robotic signals such as control actions and proprioceptive information. To address the issue, we …

0 citations arXiv (Cornell University)
Accès ouvert 2025 preprint OpenAlex

FontAdapter: Instant Font Adaptation in Visual Text Generation

M. Koo, Subin Kim, Sangkyung Kwak, Jae Hyun Nam et autres

Text-to-image diffusion models have significantly improved the seamless integration of visual text into diverse image contexts. Recent approaches further improve control over font styles through fine-tuning with predefined font dictionaries. However, adapting unseen fonts outside the preset is computationally expensive, often requiring …

0 citations arXiv (Cornell University)
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