Zobrazeno 1 - 10
of 978
pro vyhledávání: '"JIANG Kui"'
Existing face super-resolution (FSR) methods have made significant advancements, but they primarily super-resolve face with limited visual information, original pixel-wise space in particular, commonly overlooking the pluralistic clues, like the high
Externí odkaz:
http://arxiv.org/abs/2411.09293
Learning a self-supervised Monocular Depth Estimation (MDE) model with great generalization remains significantly challenging. Despite the success of adversarial augmentation in the supervised learning generalization, naively incorporating it into se
Externí odkaz:
http://arxiv.org/abs/2411.02149
Image restoration (IR) refers to the process of improving visual quality of images while removing degradation, such as noise, blur, weather effects, and so on. Traditional IR methods typically target specific types of degradation, which limits their
Externí odkaz:
http://arxiv.org/abs/2410.15067
Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering. However, with sparse input views, the lack of multi-view consistency constraints results in poorly initialized p
Externí odkaz:
http://arxiv.org/abs/2410.11394
Autor:
Jin, Xin, Guo, Chunle, Li, Xiaoming, Yue, Zongsheng, Li, Chongyi, Zhou, Shangchen, Feng, Ruicheng, Dai, Yuekun, Yang, Peiqing, Loy, Chen Change, Li, Ruoqi, Liu, Chang, Wang, Ziyi, Du, Yao, Yang, Jingjing, Bao, Long, Sun, Heng, Kong, Xiangyu, Xing, Xiaoxia, Wu, Jinlong, Xue, Yuanyang, Park, Hyunhee, Song, Sejun, Kim, Changho, Tan, Jingfan, Luo, Wenhan, Liu, Zikun, Qiao, Mingde, Jiang, Junjun, Jiang, Kui, Xiao, Yao, Sun, Chuyang, Hu, Jinhui, Ruan, Weijian, Dong, Yubo, Chen, Kai, Jo, Hyejeong, Qin, Jiahao, Han, Bingjie, Qin, Pinle, Chai, Rui, Wang, Pengyuan
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data fo
Externí odkaz:
http://arxiv.org/abs/2406.07006
Neural Radiance Fields (NeRF) with hybrid representations have shown impressive capabilities in reconstructing scenes for view synthesis, delivering high efficiency. Nonetheless, their performance significantly drops with sparse view inputs, due to t
Externí odkaz:
http://arxiv.org/abs/2406.07828
Recently, spiking neural networks (SNNs) have demonstrated substantial potential in computer vision tasks. In this paper, we present an Efficient Spiking Deraining Network, called ESDNet. Our work is motivated by the observation that rain pixel value
Externí odkaz:
http://arxiv.org/abs/2405.06277
Recent progress in remote sensing image (RSI) super-resolution (SR) has exhibited remarkable performance using deep neural networks, e.g., Convolutional Neural Networks and Transformers. However, existing SR methods often suffer from either a limited
Externí odkaz:
http://arxiv.org/abs/2405.04964
Autor:
Spencer, Jaime, Tosi, Fabio, Poggi, Matteo, Arora, Ripudaman Singh, Russell, Chris, Hadfield, Simon, Bowden, Richard, Zhou, GuangYuan, Li, ZhengXin, Rao, Qiang, Bao, YiPing, Liu, Xiao, Kim, Dohyeong, Kim, Jinseong, Kim, Myunghyun, Lavreniuk, Mykola, Li, Rui, Mao, Qing, Wu, Jiang, Zhu, Yu, Sun, Jinqiu, Zhang, Yanning, Patni, Suraj, Agarwal, Aradhye, Arora, Chetan, Sun, Pihai, Jiang, Kui, Wu, Gang, Liu, Jian, Liu, Xianming, Jiang, Junjun, Zhang, Xidan, Wei, Jianing, Wang, Fangjun, Tan, Zhiming, Wang, Jiabao, Luginov, Albert, Shahzad, Muhammad, Hosseini, Seyed, Trajcevski, Aleksander, Elder, James H.
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settin
Externí odkaz:
http://arxiv.org/abs/2404.16831
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints. However, its effectiveness is hampered when working with sparsely available views, often leading to performance dips due to overfitting. FreeNeRF attem
Externí odkaz:
http://arxiv.org/abs/2404.00992