Zobrazeno 1 - 10
of 22 591
pro vyhledávání: '"Lee, In Jae"'
Autor:
Seo, Minseok, Nguyen, Xuan Truong, Hwang, Seok Joong, Kwon, Yongkee, Kim, Guhyun, Park, Chanwook, Kim, Ilkon, Park, Jaehan, Kim, Jeongbin, Shin, Woojae, Won, Jongsoon, Choi, Haerang, Kim, Kyuyoung, Kwon, Daehan, Jeong, Chunseok, Lee, Sangheon, Choi, Yongseok, Byun, Wooseok, Baek, Seungcheol, Lee, Hyuk-Jae, Kim, John
Publikováno v:
ASPLOS 2024
Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, diverse compute characteristics of end-to-end LLM inference present challenges as previously proposed
Externí odkaz:
http://arxiv.org/abs/2410.15008
Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. However, MGMs still underperform in image synthesis compared to recent we
Externí odkaz:
http://arxiv.org/abs/2410.13136
As latent diffusion models (LDMs) democratize image generation capabilities, there is a growing need to detect fake images. A good detector should focus on the generative models fingerprints while ignoring image properties such as semantic content, r
Externí odkaz:
http://arxiv.org/abs/2410.11835
Autor:
Cai, Mu, Tan, Reuben, Zhang, Jianrui, Zou, Bocheng, Zhang, Kai, Yao, Feng, Zhu, Fangrui, Gu, Jing, Zhong, Yiwu, Shang, Yuzhang, Dou, Yao, Park, Jaden, Gao, Jianfeng, Lee, Yong Jae, Yang, Jianwei
Understanding fine-grained temporal dynamics is crucial for multimodal video comprehension and generation. Due to the lack of fine-grained temporal annotations, existing video benchmarks mostly resemble static image benchmarks and are incompetent at
Externí odkaz:
http://arxiv.org/abs/2410.10818
There has been growing sentiment recently that modern large multimodal models (LMMs) have addressed most of the key challenges related to short video comprehension. As a result, both academia and industry are gradually shifting their attention toward
Externí odkaz:
http://arxiv.org/abs/2410.02763
Autor:
Li, Yuheng, Liu, Haotian, Cai, Mu, Li, Yijun, Shechtman, Eli, Lin, Zhe, Lee, Yong Jae, Singh, Krishna Kumar
In this paper, we introduce a model designed to improve the prediction of image-text alignment, targeting the challenge of compositional understanding in current visual-language models. Our approach focuses on generating high-quality training dataset
Externí odkaz:
http://arxiv.org/abs/2410.00905
Autor:
Oh, Yujin, Park, Sangjoon, Li, Xiang, Yi, Wang, Paly, Jonathan, Efstathiou, Jason, Chan, Annie, Kim, Jun Won, Byun, Hwa Kyung, Lee, Ik Jae, Cho, Jaeho, Wee, Chan Woo, Shu, Peng, Wang, Peilong, Yu, Nathan, Holmes, Jason, Ye, Jong Chul, Li, Quanzheng, Liu, Wei, Koom, Woong Sub, Kim, Jin Sung, Kim, Kyungsang
Clinical experts employ diverse philosophies and strategies in patient care, influenced by regional patient populations. However, existing medical artificial intelligence (AI) models are often trained on data distributions that disproportionately ref
Externí odkaz:
http://arxiv.org/abs/2410.00046
Autor:
Shang, Yuzhang, Xu, Bingxin, Kang, Weitai, Cai, Mu, Li, Yuheng, Wen, Zehao, Dong, Zhen, Keutzer, Kurt, Lee, Yong Jae, Yan, Yan
Advancements in Large Language Models (LLMs) inspire various strategies for integrating video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface linking sophisticated video encoders to LLMs. However, due to computati
Externí odkaz:
http://arxiv.org/abs/2409.12963
3D perception in LiDAR point clouds is crucial for a self-driving vehicle to properly act in 3D environment. However, manually labeling point clouds is hard and costly. There has been a growing interest in self-supervised pre-training of 3D perceptio
Externí odkaz:
http://arxiv.org/abs/2409.06827
LaTeX is suitable for creating specially formatted documents in science, technology, mathematics, and computer science. Although the use of mathematical expressions in LaTeX format along with language models is increasing, there are no proper evaluat
Externí odkaz:
http://arxiv.org/abs/2409.06639