1 citations · 1 across the 3 of their papers we have counts for
4 papers
GuideCAD: A Lightweight Multimodal Framework for 3D CAD Model Generation via Prefix Embedding
Minseong Kim, Jinyeong Park, Sungho Park +1
Multi-modal approaches used for 3D CAD generation require substantial computational resources, necessitating efficient training. To address this, we propose GuideCAD, which leverag…
QATMA: Quantization-Aware Training with Multimodal Alignment for Open-Vocabulary Object Detection
Jinyeong Park, Donghwa Kang, Seunghwan An +4
Quantizing open-vocabulary object detection (OVOD) models reduces their memory and computational costs, but extremely low-bit quantization severely degrades both cross-modal (regio…
ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models
Minseop Jung, Minseong Kim, Jibum Kim
The success of Transformer-based models has encouraged many researchers to learn CAD models using sequence-based approaches. However, learning CAD models is still a challenge, beca…
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation
Jinyeong Park, Jaegyoon Ahn, Jonghwan Choi +1
Optimizing techniques for discovering molecular structures with desired properties is crucial in artificial intelligence(AI)-based drug discovery. Combining deep generative models…