Zobrazeno 1 - 10
of 5 056
pro vyhledávání: '"Li, Wenbo"'
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:
He, Dan, Wang, Guofen, Li, Weisheng, Shu, Yucheng, Li, Wenbo, Yang, Lijian, Huang, Yuping, Li, Feiyan
Multimodal image fusion (MMIF) aims to integrate information from different modalities to obtain a comprehensive image, aiding downstream tasks. However, existing methods tend to prioritize natural image fusion and focus on information complementary
Externí odkaz:
http://arxiv.org/abs/2411.10036
Autor:
Li, Wenbo, Li, Guohao, Lan, Zhibin, Xu, Xue, Zhuang, Wanru, Liu, Jiachen, Xiao, Xinyan, Su, Jinsong
Diffusion-based text-to-image models have demonstrated impressive achievements in diversity and aesthetics but struggle to generate images with legible visual texts. Existing backbone models have limitations such as misspelling, failing to generate t
Externí odkaz:
http://arxiv.org/abs/2410.04439
Autor:
Liu, Kai, Zhang, Ziqing, Li, Wenbo, Pei, Renjing, Song, Fenglong, Liu, Xiaohong, Kong, Linghe, Zhang, Yulun
Image quality assessment (IQA) serves as the golden standard for all models' performance in nearly all computer vision fields. However, it still suffers from poor out-of-distribution generalization ability and expensive training costs. To address the
Externí odkaz:
http://arxiv.org/abs/2410.02505
Recently, when dealing with high-resolution images, dominant LMMs usually divide them into multiple local images and one global image, which will lead to a large number of visual tokens. In this work, we introduce AVG-LLaVA, an LMM that can adaptivel
Externí odkaz:
http://arxiv.org/abs/2410.02745
Autor:
Zhou, Kun, Lin, Xinyu, Li, Wenbo, Xu, Xiaogang, Cai, Yuanhao, Liu, Zhonghang, Han, Xiaoguang, Lu, Jiangbo
Previous low-light image enhancement (LLIE) approaches, while employing frequency decomposition techniques to address the intertwined challenges of low frequency (e.g., illumination recovery) and high frequency (e.g., noise reduction), primarily focu
Externí odkaz:
http://arxiv.org/abs/2409.01641
Autor:
Di, Xin, Peng, Long, Xia, Peizhe, Li, Wenbo, Pei, Renjing, Cao, Yang, Wang, Yang, Zha, Zheng-Jun
Burst super-resolution aims to reconstruct high-resolution images with higher quality and richer details by fusing the sub-pixel information from multiple burst low-resolution frames. In BusrtSR, the key challenge lies in extracting the base frame's
Externí odkaz:
http://arxiv.org/abs/2408.08665
Diffusion models have demonstrated remarkable and robust abilities in both image and video generation. To achieve greater control over generated results, researchers introduce additional architectures, such as ControlNet, Adapters and ReferenceNet, t
Externí odkaz:
http://arxiv.org/abs/2408.06070
Autor:
Chen, Haoyu, Li, Wenbo, Gu, Jinjin, Ren, Jingjing, Chen, Sixiang, Ye, Tian, Pei, Renjing, Zhou, Kaiwen, Song, Fenglong, Zhu, Lei
Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light. Traditional image restoration methods require manual selection of specific tasks, algorithms, and execution sequences, whic
Externí odkaz:
http://arxiv.org/abs/2407.18035
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