2 code implementations • 9 Apr 2024 • Zhengqing Gao, Xu-Yao Zhang, Cheng-Lin Liu
To address these issues, we propose a simple but effective framework called unified entropy optimization (UniEnt), which is capable of simultaneously adapting to covariate-shifted in-distribution (csID) data and detecting covariate-shifted out-of-distribution (csOOD) data.
no code implementations • 11 Mar 2024 • Haoru Tan, Chuang Wang, Sitong Wu, Xu-Yao Zhang, Fei Yin, Cheng-Lin Liu
In this paper, we propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
1 code implementation • 7 Mar 2024 • Shijie Ma, Fei Zhu, Zhun Zhong, Xu-Yao Zhang, Cheng-Lin Liu
Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some labeled data of old classes.
1 code implementation • 5 Mar 2024 • Fei Zhu, Xu-Yao Zhang, Zhen Cheng, Cheng-Lin Liu
Reliable confidence estimation is a challenging yet fundamental requirement in many risk-sensitive applications.
no code implementations • 4 Mar 2024 • Fei Zhu, Shijie Ma, Zhen Cheng, Xu-Yao Zhang, Zhaoxiang Zhang, Cheng-Lin Liu
This paper aims to provide a comprehensive introduction to the emerging open-world machine learning paradigm, to help researchers build more powerful AI systems in their respective fields, and to promote the development of artificial general intelligence.
no code implementations • 4 Jan 2024 • Haiyang Guo, Fei Zhu, Wenzhuo LIU, Xu-Yao Zhang, Cheng-Lin Liu
On the other hand, our approach utilizes a pre-trained model as the backbone and utilizes LoRA to fine-tune with a tiny amount of parameters when learning new classes.
1 code implementation • 22 Nov 2023 • Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu
Previous methods mostly take post-training score transformation or hybrid models to ensure low scores on OOD inputs while separating known classes.
no code implementations • 12 Sep 2023 • Jiao Zhang, Xu-Yao Zhang, Cheng-Lin Liu
We advocate that researchers in the DG community refer to dynamic performance of methods for more comprehensive and reliable evaluation.
1 code implementation • 18 Jul 2023 • Shijie Ma, Fei Zhu, Zhen Cheng, Xu-Yao Zhang
By distilling both InD samples and outliers, the condensed datasets are capable to train models competent in both InD classification and OOD detection.
1 code implementation • CVPR 2023 • Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu
Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications.
1 code implementation • 21 Mar 2023 • Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu
Label noise poses a serious threat to deep neural networks (DNNs).
1 code implementation • 6 Mar 2023 • Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu
We investigate this problem and reveal that popular confidence calibration methods often lead to worse confidence separation between correct and incorrect samples, making it more difficult to decide whether to trust a prediction or not.
no code implementations • 2 Mar 2023 • Zhen Cheng, Fei Zhu, Xu-Yao Zhang, Cheng-Lin Liu
Detecting Out-of-distribution (OOD) inputs have been a critical issue for neural networks in the open world.
no code implementations • 26 Mar 2022 • Zhuang Qian, Kaizhu Huang, Qiu-Feng Wang, Xu-Yao Zhang
In this paper, we present a comprehensive survey trying to offer a systematic and structured investigation on robust adversarial training in pattern recognition.
1 code implementation • 20 Mar 2022 • Guo-Wang Xie, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu
In this paper, we propose a simple yet effective approach to rectify distorted document image by estimating control points and reference points.
no code implementations • 14 Jan 2022 • Yuqi Wang, Xu-Yao Zhang, Cheng-Lin Liu, Zhaoxiang Zhang
Moreover, through experiments we show that discrete language representation has several advantages compared with continuous feature representation, from the aspects of interpretability, generalization, and robustness.
2 code implementations • NeurIPS 2021 • Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu
Deep learning systems typically suffer from catastrophic forgetting of past knowledge when acquiring new skills continually.
no code implementations • AAAI 2021 • Haoru Tan, Chuang Wang, Sitong Wu, Tie-Qiang Wang, Xu-Yao Zhang, Cheng-Lin Liu
It consists of three parts: a graph neural network to generate a high-level local feature, an attention-based module to normalize the rotational transform, and a global feature matching module based on proximal optimization.
no code implementations • 29 Sep 2021 • Zhen Cheng, Fei Zhu, Xu-Yao Zhang, Cheng-Lin Liu
Comprehensive experiments demonstrate that FSR is effective to alleviate the dominance of larger eigenvalues and improve adversarial robustness on different datasets.
1 code implementation • CVPR 2021 • Fei Zhu, Xu-Yao Zhang, Chuang Wang, Fei Yin, Cheng-Lin Liu
Despite the impressive performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning new tasks incrementally.
no code implementations • CVPR 2021 • Wei Feng, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu
To overcome the lack of character-level annotations, we propose a novel weakly-supervised character center detection module, which only uses word-level annotated real images to generate character-level labels.
1 code implementation • 14 Apr 2021 • Guo-Wang Xie, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu
As camera-based documents are increasingly used, the rectification of distorted document images becomes a need to improve the recognition performance.
no code implementations • 1 Jan 2021 • Fei Zhu, Xu-Yao Zhang, Chuang Wang, Cheng-Lin Liu
In spite of the simplicity, extensive experiments demonstrate that the misclassification detection performance of DNNs can be significantly improved by seeing more generated pseudo-classes during training.
no code implementations • 1 Dec 2020 • Mengbiao Zhao, Wei Feng, Fei Yin, Xu-Yao Zhang, Cheng-Lin Liu
We propose an Expectation-Maximization (EM) based weakly-supervised learning framework to train an accurate arbitrary-shaped text detector using only a small amount of polygon-level annotated data combined with a large amount of weakly annotated data.
no code implementations • 12 Jun 2020 • Xu-Yao Zhang, Cheng-Lin Liu, Ching Y. Suen
The accuracies for many pattern recognition tasks have increased rapidly year by year, achieving or even outperforming human performance.
no code implementations • 12 May 2019 • Danlu Chen, Xu-Yao Zhang, Wei zhang, Yao Lu, Xiuli Li, Tao Mei
Taking scene text detection as the application, where no suitable ensemble learning strategy exists, PEL can significantly improve the performance, compared to either individual state-of-the-art models, or the fusion of multiple models by non-maximum suppression.
no code implementations • 2 Jun 2018 • Yi-Chao Wu, Fei Yin, Xu-Yao Zhang, Li Liu, Cheng-Lin Liu
Scene text recognition has drawn great attentions in the community of computer vision and artificial intelligence due to its challenges and wide applications.
3 code implementations • CVPR 2018 • Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, Cheng-Lin Liu
To improve the robustness, we propose a novel learning framework called convolutional prototype learning (CPL).
1 code implementation • 27 Oct 2017 • Xiao-Bo Jin, Xu-Yao Zhang, Kai-Zhu Huang, Guang-Gang Geng
Conjugate gradient (CG) methods are a class of important methods for solving linear equations and nonlinear optimization problems.
no code implementations • 6 Sep 2017 • Fei Yin, Yi-Chao Wu, Xu-Yao Zhang, Cheng-Lin Liu
In this paper, we investigate the intrinsic characteristics of text recognition, and inspired by human cognition mechanisms in reading texts, we propose a scene text recognition method with character models on convolutional feature map.
no code implementations • ICCV 2017 • Wenhao He, Xu-Yao Zhang, Fei Yin, Cheng-Lin Liu
To verify this point of view, we propose a deep direct regression based method for multi-oriented scene text detection.
1 code implementation • 21 Jun 2016 • Xu-Yao Zhang, Fei Yin, Yan-Ming Zhang, Cheng-Lin Liu, Yoshua Bengio
In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters.
no code implementations • 18 Jun 2016 • Xu-Yao Zhang, Yoshua Bengio, Cheng-Lin Liu
Furthermore, although directMap+convNet can achieve the best results and surpass human-level performance, we show that writer adaptation in this case is still effective.
Data Augmentation Offline Handwritten Chinese Character Recognition
no code implementations • 29 Jan 2016 • Guo-Sen Xie, Xu-Yao Zhang, Shuicheng Yan, Cheng-Lin Liu
Learned from a large-scale training dataset, CNN features are much more discriminative and accurate than the hand-crafted features.
no code implementations • ICCV 2015 • Guo-Sen Xie, Xu-Yao Zhang, Xiangbo Shu, Shuicheng Yan, Cheng-Lin Liu
Feature pooling is an important strategy to achieve high performance in image classification.