no code implementations • 1 Apr 2024 • Yuru Xiao, Xianming Liu, Deming Zhai, Kui Jiang, Junjun Jiang, Xiangyang Ji
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints.
no code implementations • 13 Dec 2023 • Xiong Zhou, Xianming Liu, Hanzhang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji
In this paper, we introduce the unhinged loss, a concise loss function, that offers more mathematical opportunities to analyze the closed-form dynamics while requiring as few simplifications or assumptions as possible.
no code implementations • 28 May 2023 • Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji
Large amounts of incremental learning algorithms have been proposed to alleviate the catastrophic forgetting issue arises while dealing with sequential data on a time series.
1 code implementation • 6 Apr 2023 • Chenyang Wang, Junjun Jiang, Zhiwei Zhong, Deming Zhai, Xianming Liu
In this paper, we build a novel parsing map guided face super-resolution network which extracts the face prior (i. e., parsing map) directly from low-resolution face image for the following utilization.
no code implementations • 23 Jun 2022 • Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji
We verify the effectiveness of PAL on class-imbalanced learning and noise-tolerant learning by extensive experiments on synthetic and real-world datasets.
no code implementations • ICLR 2022 • Xiong Zhou, Xianming Liu, Deming Zhai, Junjun Jiang, Xin Gao, Xiangyang Ji
One of the main challenges for feature representation in deep learning-based classification is the design of appropriate loss functions that exhibit strong discriminative power.
1 code implementation • CVPR 2022 • Yiqi Zhong, Xianming Liu, Deming Zhai, Junjun Jiang, Xiangyang Ji
A new type of non-invasive attacks emerged recently, which attempt to cast perturbation onto the target by optics based tools, such as laser beam and projector.
1 code implementation • ICCV 2021 • Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, Xiangyang Ji
In this paper, we theoretically prove that \textbf{any loss can be made robust to noisy labels} by restricting the network output to the set of permutations over a fixed vector.
no code implementations • 15 Oct 2020 • Bo Pang, Deming Zhai, Junjun Jiang, Xianming Liu
In this work, we propose a novel selective contrastive learning framework for unsupervised feature learning.
no code implementations • 9 Jun 2020 • Bo Pang, Deming Zhai, Junjun Jiang, Xian-Ming Liu
Image enhancement from degradation of rainy artifacts plays a critical role in outdoor visual computing systems.
no code implementations • 17 Mar 2020 • Ruifeng Shi, Deming Zhai, Xian-Ming Liu, Junjun Jiang, Wen Gao
However, the performance of CNN-based classification approach depends on a large amount of high-quality manually labeled training data, which are inevitably introduced noise on labels in practice, leading to model overfitting and performance degradation.
no code implementations • 4 Mar 2020 • Yongsen Zhao, Deming Zhai, Junjun Jiang, Xian-Ming Liu
Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation.