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pro vyhledávání: '"Kim, Jin‐Young"'
Beta-amyloid positron emission tomography (A$\beta$-PET) imaging has become a critical tool in Alzheimer's disease (AD) research and diagnosis, providing insights into the pathological accumulation of amyloid plaques, one of the hallmarks of AD. Howe
Externí odkaz:
http://arxiv.org/abs/2409.18282
Autor:
Lee, Hyeongmin, Kim, Jin-Young, Baek, Kyungjune, Kim, Jihwan, Go, Hyojun, Ha, Seongsu, Han, Seokjin, Jang, Jiho, Jung, Raehyuk, Kim, Daewoo, Kim, GeunOh, Kim, JongMok, Kim, Jongseok, Kim, Junwan, Kwon, Soonwoo, Lee, Jangwon, Park, Seungjoon, Seo, Minjoon, Suh, Jay, Yi, Jaehyuk, Lee, Aiden
In this work, we discuss evaluating video foundation models in a fair and robust manner. Unlike language or image foundation models, many video foundation models are evaluated with differing parameters (such as sampling rate, number of frames, pretra
Externí odkaz:
http://arxiv.org/abs/2408.11318
Autor:
Kim, Hyunwoo, Choi, Yoonseo, Yang, Taehyun, Lee, Honggu, Park, Chaneon, Lee, Yongju, Kim, Jin Young, Kim, Juho
With large language models (LLMs), conversational search engines shift how users retrieve information from the web by enabling natural conversations to express their search intents over multiple turns. Users' natural conversation embodies rich but im
Externí odkaz:
http://arxiv.org/abs/2407.13166
We present Diffusion Model Patching (DMP), a simple method to boost the performance of pre-trained diffusion models that have already reached convergence, with a negligible increase in parameters. DMP inserts a small, learnable set of prompts into th
Externí odkaz:
http://arxiv.org/abs/2405.17825
Autor:
Kim, Jin Young
Publikováno v:
Eur. Phys. J. C (2024) 84:1070
We consider the bending of light around a compact astrophysical object with both the electric field and the magnetic field in Einstein-Born-Infeld theory. From the null geodesic of a light ray passing a massive object with electric charge and magneti
Externí odkaz:
http://arxiv.org/abs/2404.14756
Autor:
Jung, Raehyuk, Go, Hyojun, Yi, Jaehyuk, Jang, Jiho, Kim, Daniel, Suh, Jay, Lee, Aiden, Han, Cooper, Lee, Jae, Kim, Jeff, Kim, Jin-Young, Kim, Junwan, Park, Kyle, Lee, Lucas, Ha, Mars, Seo, Minjoon, Jo, Abraham, Park, Ed, Kianinejad, Hassan, Kim, SJ, Moon, Tony, Jeong, Wade, Popescu, Andrei, Kim, Esther, Yoon, EK, Heo, Genie, Choi, Henry, Kang, Jenna, Han, Kevin, Seo, Noah, Nguyen, Sunny, Won, Ryan, Park, Yeonhoo, Giuliani, Anthony, Chung, Dave, Yoon, Hans, Le, James, Ahn, Jenny, Lee, June, Saini, Maninder, Sanders, Meredith, Lee, Soyoung, Kim, Sue, Couture, Travis
This technical report introduces Pegasus-1, a multimodal language model specialized in video content understanding and interaction through natural language. Pegasus-1 is designed to address the unique challenges posed by video data, such as interpret
Externí odkaz:
http://arxiv.org/abs/2404.14687
Diffusion-based generative models have emerged as powerful tools in the realm of generative modeling. Despite extensive research on denoising across various timesteps and noise levels, a conflict persists regarding the relative difficulties of the de
Externí odkaz:
http://arxiv.org/abs/2403.10348
Diffusion models have achieved remarkable success across a range of generative tasks. Recent efforts to enhance diffusion model architectures have reimagined them as a form of multi-task learning, where each task corresponds to a denoising task at a
Externí odkaz:
http://arxiv.org/abs/2403.09176
Recent progress in single-image 3D generation highlights the importance of multi-view coherency, leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale images. However, the aspect of novel-view diversity remains underexpl
Externí odkaz:
http://arxiv.org/abs/2312.15980
Diffusion models generate highly realistic images by learning a multi-step denoising process, naturally embodying the principles of multi-task learning (MTL). Despite the inherent connection between diffusion models and MTL, there remains an unexplor
Externí odkaz:
http://arxiv.org/abs/2310.07138