Zobrazeno 1 - 10
of 12 266
pro vyhledávání: '"Guo, Yong"'
Autor:
Yin, Rui, Qin, Haotong, Zhang, Yulun, Li, Wenbo, Guo, Yong, Zhu, Jianjun, Wang, Cheng, Jia, Biao
Dense prediction is a critical task in computer vision. However, previous methods often require extensive computational resources, which hinders their real-world application. In this paper, we propose BiDense, a generalized binary neural network (BNN
Externí odkaz:
http://arxiv.org/abs/2411.10346
Autor:
Qiao, Junbo, Liao, Jincheng, Li, Wei, Zhang, Yulun, Guo, Yong, Wen, Yi, Qiu, Zhangxizi, Xie, Jiao, Hu, Jie, Lin, Shaohui
State Space Models (SSM), such as Mamba, have shown strong representation ability in modeling long-range dependency with linear complexity, achieving successful applications from high-level to low-level vision tasks. However, SSM's sequential nature
Externí odkaz:
http://arxiv.org/abs/2410.10140
Autor:
Li, Jianze, Cao, Jiezhang, Zou, Zichen, Su, Xiongfei, Yuan, Xin, Zhang, Yulun, Guo, Yong, Yang, Xiaokang
Diffusion models have been achieving excellent performance for real-world image super-resolution (Real-ISR) with considerable computational costs. Current approaches are trying to derive one-step diffusion models from multi-step counterparts through
Externí odkaz:
http://arxiv.org/abs/2410.04224
Knowledge distillation aims to transfer knowledge from a large teacher model to a compact student counterpart, often coming with a significant performance gap between them. We find that a too-large performance gap can hamper the training process, whi
Externí odkaz:
http://arxiv.org/abs/2410.04140
Autor:
Cheng, Kun, Yu, Lei, Tu, Zhijun, He, Xiao, Chen, Liyu, Guo, Yong, Zhu, Mingrui, Wang, Nannan, Gao, Xinbo, Hu, Jie
Recent advances indicate that diffusion models hold great promise in image super-resolution. While the latest methods are primarily based on latent diffusion models with convolutional neural networks, there are few attempts to explore transformers, w
Externí odkaz:
http://arxiv.org/abs/2409.19589
Autor:
He, Xiao, Tang, Huaao, Tu, Zhijun, Zhang, Junchao, Cheng, Kun, Chen, Hanting, Guo, Yong, Zhu, Mingrui, Wang, Nannan, Gao, Xinbo, Hu, Jie
Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. However, these approaches typically require tens or even hundreds of iterative samplin
Externí odkaz:
http://arxiv.org/abs/2408.07476
Autor:
Sharma, Raghav, Ngo, Tung, Raimondo, Eleonora, Giordano, Anna, Igarashi, Junta, Jinnai, Butsurin, Zhao, Shishun, Lei, Jiayu, Guo, Yong-Xin, Finocchio, Giovanni, Fukami, Shunsuke, Ohno, Hideo, Yang, Hyunsoo
Publikováno v:
Nature Electronics (2024)
Radiofrequency harvesting using ambient wireless energy could be used to reduce the carbon footprint of electronic devices. However, ambient radiofrequency energy is weak (less than -20 dBm), and thermodynamic limits and high-frequency parasitic impe
Externí odkaz:
http://arxiv.org/abs/2408.01160
Real-world data often follows a long-tailed distribution, where a few head classes occupy most of the data and a large number of tail classes only contain very limited samples. In practice, deep models often show poor generalization performance on ta
Externí odkaz:
http://arxiv.org/abs/2407.04911
Autor:
Ren, Jingjing, Li, Wenbo, Chen, Haoyu, Pei, Renjing, Shao, Bin, Guo, Yong, Peng, Long, Song, Fenglong, Zhu, Lei
Ultra-high-resolution image generation poses great challenges, such as increased semantic planning complexity and detail synthesis difficulties, alongside substantial training resource demands. We present UltraPixel, a novel architecture utilizing ca
Externí odkaz:
http://arxiv.org/abs/2407.02158
Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit parameters and efficient integer/bitwise constructions for storage compression
Externí odkaz:
http://arxiv.org/abs/2406.06649