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pro vyhledávání: '"YOU, Yang"'
We present Pyramid Attention Broadcast (PAB), a real-time, high quality and training-free approach for DiT-based video generation. Our method is founded on the observation that attention difference in the diffusion process exhibits a U-shaped pattern
Externí odkaz:
http://arxiv.org/abs/2408.12588
Autor:
Li, Zekai, Guo, Ziyao, Zhao, Wangbo, Zhang, Tianle, Cheng, Zhi-Qi, Khaki, Samir, Zhang, Kaipeng, Sajedi, Ahmad, Plataniotis, Konstantinos N, Wang, Kai, You, Yang
Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this, existing methods use the agent model to extract information from the t
Externí odkaz:
http://arxiv.org/abs/2408.03360
With the advance of deep learning, much progress has been made in building powerful artificial intelligence (AI) systems for automatic Chest X-ray (CXR) analysis. Most existing AI models are trained to be a binary classifier with the aim of distingui
Externí odkaz:
http://arxiv.org/abs/2408.02214
Generative models have achieved remarkable success in image, video, and text domains. Inspired by this, researchers have explored utilizing generative models to generate neural network parameters. However, these efforts have been limited by the param
Externí odkaz:
http://arxiv.org/abs/2408.01415
Autor:
You, Yang, Uy, Mikaela Angelina, Han, Jiaqi, Thomas, Rahul, Zhang, Haotong, You, Suya, Guibas, Leonidas
Reverse engineering 3D computer-aided design (CAD) models from images is an important task for many downstream applications including interactive editing, manufacturing, architecture, robotics, etc. The difficulty of the task lies in vast representat
Externí odkaz:
http://arxiv.org/abs/2408.01437
Autor:
Liu, Ziming, Wang, Shaoyu, Cheng, Shenggan, Zhao, Zhongkai, Zhao, Xuanlei, Demmel, James, You, Yang
In recent years, Transformer-based Large Language Models (LLMs) have garnered significant attention due to their exceptional performance across a variety of tasks. However, training these models on long sequences presents a substantial challenge in t
Externí odkaz:
http://arxiv.org/abs/2407.00611
Autor:
Zhang, Tianle, Ma, Langtian, Yan, Yuchen, Zhang, Yuchen, Wang, Kai, Yang, Yue, Guo, Ziyao, Shao, Wenqi, You, Yang, Qiao, Yu, Luo, Ping, Zhang, Kaipeng
Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. Despite these strides, evaluating these models poses substantial challenges. Pr
Externí odkaz:
http://arxiv.org/abs/2406.08845
Autor:
Ni, Jinjie, Xue, Fuzhao, Yue, Xiang, Deng, Yuntian, Shah, Mahir, Jain, Kabir, Neubig, Graham, You, Yang
Evaluating large language models (LLMs) is challenging. Traditional ground-truth-based benchmarks fail to capture the comprehensiveness and nuance of real-world queries, while LLM-as-judge benchmarks suffer from grading biases and limited query quant
Externí odkaz:
http://arxiv.org/abs/2406.06565
Autor:
Qin, Ziheng, Xu, Zhaopan, Zhou, Yukun, Zheng, Zangwei, Cheng, Zebang, Tang, Hao, Shang, Lei, Sun, Baigui, Peng, Xiaojiang, Timofte, Radu, Yao, Hongxun, Wang, Kai, You, Yang
Deep learning benefits from the growing abundance of available data. Meanwhile, efficiently dealing with the growing data scale has become a challenge. Data publicly available are from different sources with various qualities, and it is impractical t
Externí odkaz:
http://arxiv.org/abs/2405.18347
Autor:
Wang, Kai, Zhou, Yukun, Shi, Mingjia, Yuan, Zhihang, Shang, Yuzhang, Peng, Xiaojiang, Zhang, Hanwang, You, Yang
Training diffusion models is always a computation-intensive task. In this paper, we introduce a novel speed-up method for diffusion model training, called, which is based on a closer look at time steps. Our key findings are: i) Time steps can be empi
Externí odkaz:
http://arxiv.org/abs/2405.17403