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Contribution-based Low-Rank Adaptation with Pre-training Model for Real Image Restoration

Autor: Park, Donwon, Kim, Hayeon, Chun, Se Young

Recently, pre-trained model and efficient parameter tuning have achieved remarkable success in natural language processing and high-level computer vision with the aid of masked modeling and prompt tuning. In low-level computer vision, however, there

Externí odkaz: http://arxiv.org/abs/2408.01099

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BeyondScene: Higher-Resolution Human-Centric Scene Generation With Pretrained Diffusion

Autor: Kim, Gwanghyun, Kim, Hayeon, Seo, Hoigi, Kang, Dong Un, Chun, Se Young

Generating higher-resolution human-centric scenes with details and controls remains a challenge for existing text-to-image diffusion models. This challenge stems from limited training image size, text encoder capacity (limited tokens), and the inhere

Externí odkaz: http://arxiv.org/abs/2404.04544

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Improvement in Variational Quantum Algorithms by Measurement Simplification

Autor: Hahm, Jaehoon, Kim, Hayeon, Park, Young June

Variational Quantum Algorithms (VQAs) are expected to be promising algorithms with quantum advantages that can be run at quantum computers in the close future. In this work, we review simple rules in basic quantum circuits, and propose a simplificati

Externí odkaz: http://arxiv.org/abs/2312.06176

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DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model

Autor: Seo, Hoigi, Kim, Hayeon, Kim, Gwanghyun, Chun, Se Young

The increasing demand for high-quality 3D content creation has motivated the development of automated methods for creating 3D object models from a single image and/or from a text prompt. However, the reconstructed 3D objects using state-of-the-art im

Externí odkaz: http://arxiv.org/abs/2304.02827

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