Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Go, Hyojun"'
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
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:
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
In this paper, we address the performance degradation of efficient diffusion models by introducing Multi-architecturE Multi-Expert diffusion models (MEME). We identify the need for tailored operations at different time-steps in diffusion processes an
Externí odkaz:
http://arxiv.org/abs/2306.04990
Self-supervised contrastive learning (CL) has achieved state-of-the-art performance in representation learning by minimizing the distance between positive pairs while maximizing that of negative ones. Recently, it has been verified that the model lea
Externí odkaz:
http://arxiv.org/abs/2306.04175
Autor:
Go, Hyojun, Kim, JinYoung, Lee, Yunsung, Lee, Seunghyun, Oh, Shinhyeok, Moon, Hyeongdon, Choi, Seungtaek
Diffusion-based generative models have achieved remarkable success in various domains. It trains a shared model on denoising tasks that encompass different noise levels simultaneously, representing a form of multi-task learning (MTL). However, analyz
Externí odkaz:
http://arxiv.org/abs/2306.00354