1 code implementation • 29 Sep 2024 • Xiaofeng Cong, Jing Zhang, Yeying Jin, JunMing Hou, Yu Zhao, Jie Gui, James Tin-Yau Kwok, Yuan Yan Tang
ColorCode offers three key features: 1) color enhancement, producing an enhanced image with a fixed color; 2) color adaptation, enabling controllable adjustments of long-wavelength color components using guidance images; and 3) color interpolation, allowing for the smooth generation of multiple colors through continuous sampling of the color code.
no code implementations • 10 Sep 2024 • Siyu Zhai, Zhibo He, Xiaofeng Cong, JunMing Hou, Jie Gui, Jian Wei You, Xin Gong, James Tin-Yau Kwok, Yuan Yan Tang
In this paper, we propose a general adversarial attack protocol.
no code implementations • 30 May 2024 • Xiaofeng Cong, Yu Zhao, Jie Gui, JunMing Hou, DaCheng Tao
Underwater image enhancement (UIE) presents a significant challenge within computer vision research.
1 code implementation • 19 Apr 2024 • JunMing Hou, ZiHan Cao, Naishan Zheng, Xuan Li, Xiaoyu Chen, Xinyang Liu, Xiaofeng Cong, Man Zhou, Danfeng Hong
In this way, our proposed method is capable of benefiting the cascaded modeling rule while achieving favorable performance in the efficient manner.
1 code implementation • CVPR 2024 • Xiaofeng Cong, Jie Gui, Jing Zhang, JunMing Hou, Hao Shen
There are two distinctions between nighttime and daytime haze.
1 code implementation • CVPR 2024 • Naishan Zheng, Man Zhou, Jie Huang, JunMing Hou, Haoying Li, Yuan Xu, Feng Zhao
To bridge this gap we introduce a Synergistic High-order Interaction Paradigm (SHIP) designed to systematically investigate spatial fine-grained and global statistics collaborations between infrared and visible images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication mathematically equivalent to global interactions and then foster high-order formats by iteratively aggregating and evolving complementary information enhancing both efficiency and flexibility.
no code implementations • 10 Apr 2023 • ZiHan Cao, ShiQi Cao, Xiao Wu, JunMing Hou, Ran Ran, Liang-Jian Deng
Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability.