Zobrazeno 1 - 10
of 734
pro vyhledávání: '"wang, Boyang"'
Autor:
Deng, Jingyang, Shen, Zhengyang, Wang, Boyang, Su, Lixin, Cheng, Suqi, Nie, Ying, Wang, Junfeng, Yin, Dawei, Ma, Jinwen
The development of Long-Context Large Language Models (LLMs) has markedly advanced natural language processing by facilitating the process of textual data across long documents and multiple corpora. However, Long-Context LLMs still face two critical
Externí odkaz:
http://arxiv.org/abs/2410.06886
Autor:
Mai, Xinji, Lin, Junxiong, Wang, Haoran, Tao, Zeng, Wang, Yan, Yan, Shaoqi, Tong, Xuan, Yu, Jiawen, Wang, Boyang, Zhou, Ziheng, Zhao, Qing, Gao, Shuyong, Zhang, Wenqiang
In the field of affective computing, fully leveraging information from a variety of sensory modalities is essential for the comprehensive understanding and processing of human emotions. Inspired by the process through which the human brain handles em
Externí odkaz:
http://arxiv.org/abs/2407.15590
Autor:
Wang, Boyang, Sridhar, Nikhil, Feng, Chao, Van der Merwe, Mark, Fishman, Adam, Fazeli, Nima, Park, Jeong Joon
We propose a robot learning method for communicating, planning, and executing a wide range of tasks, dubbed This&That. We achieve robot planning for general tasks by leveraging the power of video generative models trained on internet-scale data conta
Externí odkaz:
http://arxiv.org/abs/2407.05530
Autor:
Wang, Haoran, Mai, Xinji, Tao, Zeng, Tong, Xuan, Lin, Junxiong, Wang, Yan, Yu, Jiawen, Wang, Boyang, Yan, Shaoqi, Zhao, Qing, Zhou, Ziheng, Gao, Shuyong, Zhang, Wenqiang
The contemporary state-of-the-art of Dynamic Facial Expression Recognition (DFER) technology facilitates remarkable progress by deriving emotional mappings of facial expressions from video content, underpinned by training on voluminous datasets. Yet,
Externí odkaz:
http://arxiv.org/abs/2406.16473
Autor:
Lin, Junxiong, Tao, Zeng, Tong, Xuan, Mai, Xinji, Wang, Haoran, Wang, Boyang, Wang, Yan, Zhao, Qing, Yu, Jiawen, Lin, Yuxuan, Yan, Shaoqi, Gao, Shuyong, Zhang, Wenqiang
The problem of blind image super-resolution aims to recover high-resolution (HR) images from low-resolution (LR) images with unknown degradation modes. Most existing methods model the image degradation process using blur kernels. However, this explic
Externí odkaz:
http://arxiv.org/abs/2406.16459
Autor:
Chai, Linzheng, Liu, Shukai, Yang, Jian, Yin, Yuwei, Jin, Ke, Liu, Jiaheng, Sun, Tao, Zhang, Ge, Ren, Changyu, Guo, Hongcheng, Wang, Zekun, Wang, Boyang, Wu, Xianjie, Wang, Bing, Li, Tongliang, Yang, Liqun, Duan, Sufeng, Li, Zhoujun
Code large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code challenges and corresponding test cases, serve as a standard to evaluat
Externí odkaz:
http://arxiv.org/abs/2406.07436
Deep learning-based joint source-channel coding (deep JSCC) has been demonstrated to be an effective approach for wireless image transmission. Nevertheless, most existing work adopts an autoencoder framework to optimize conventional criteria such as
Externí odkaz:
http://arxiv.org/abs/2404.17736
Autor:
Lin, Junxiong, Wang, Yan, Tao, Zeng, Wang, Boyang, Zhao, Qing, Wang, Haorang, Tong, Xuan, Mai, Xinji, Lin, Yuxuan, Song, Wei, Yu, Jiawen, Yan, Shaoqi, Zhang, Wenqiang
Pre-trained diffusion models utilized for image generation encapsulate a substantial reservoir of a priori knowledge pertaining to intricate textures. Harnessing the potential of leveraging this a priori knowledge in the context of image super-resolu
Externí odkaz:
http://arxiv.org/abs/2403.05808
While real-world anime super-resolution (SR) has gained increasing attention in the SR community, existing methods still adopt techniques from the photorealistic domain. In this paper, we analyze the anime production workflow and rethink how to use c
Externí odkaz:
http://arxiv.org/abs/2403.01598
Autor:
Guo, Hongcheng, Yang, Jian, Liu, Jiaheng, Bai, Jiaqi, Wang, Boyang, Li, Zhoujun, Zheng, Tieqiao, Zhang, Bo, peng, Junran, Tian, Qi
Log anomaly detection is a key component in the field of artificial intelligence for IT operations (AIOps). Considering log data of variant domains, retraining the whole network for unknown domains is inefficient in real industrial scenarios. However
Externí odkaz:
http://arxiv.org/abs/2401.04749