no code implementations • 7 Mar 2024 • Weihuang Liu, Xi Shen, Haolun Li, Xiuli Bi, Bo Liu, Chi-Man Pun, Xiaodong Cun
In this work, we introduce a test-time training (TTT) strategy to address the problem.
no code implementations • 2 Nov 2023 • Xiuli Bi, Bo Liu, Fan Yang, Bin Xiao, Weisheng Li, Gao Huang, Pamela C. Cosman
This paper approaches the generated image detection problem from a new perspective: Start from real images.
no code implementations • 16 Oct 2023 • Xiuli Bi, Jiaming Liang
In existing splicing forgery datasets, the insufficient semantic varieties of spliced regions cause a problem that trained detection models overfit semantic features rather than splicing traces.
1 code implementation • CVPR 2023 • Bin Xiao, Yang Hu, Bo Liu, Xiuli Bi, Weisheng Li, Xinbo Gao
Since their binarization processes are not a component of the network, the learning-based binary descriptor cannot fully utilize the advances of deep learning.
1 code implementation • CVPR 2023 • Yongchao Wang, Bin Xiao, Xiuli Bi, Weisheng Li, Xinbo Gao
Inspired by the plain contrast idea, MCF introduces two different subnets to explore and utilize the discrepancies between subnets to correct cognitive bias of the model.
no code implementations • ICCV 2021 • Xiuli Bi, Zhipeng Zhang, Bin Xiao
For detecting the tampered regions, a forgery localization generator GM is proposed based on a multi-decoder-single-task strategy.
no code implementations • ICCV 2021 • Bin Xiao, Haifeng Wu, Xiuli Bi
The proposed DTMNet is an end-to-end deep neural network with only one convolutional layer and three fully connected layers.
no code implementations • 11 Dec 2020 • Bin Xiao, Tao Geng, Xiuli Bi, Weisheng Li
In this paper, a color-related local binary pattern (cLBP) which learns the dominant patterns from the decoded LBP is proposed for color images recognition.
no code implementations • 3 Dec 2020 • Bo Liu, Ranglei Wu, Xiuli Bi, Bin Xiao, Weisheng Li, Guoyin Wang, Xinbo Gao
The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network.
1 code implementation • cvpr 2019 workshop 2019 • Xiuli Bi, Yang Wei, Bin Xiao, Weisheng Li
The core idea of the RRU-Net is to strengthen the learning way of CNN, which is inspired by the recall and the consolidation mechanism of the human brain and implemented by the propagation and the feedback process of the residual in CNN.