Search Results for author: Cheng-Lin Liu

Found 31 papers, 10 papers with code

Plane Geometry Diagram Parsing

1 code implementation19 May 2022 Ming-Liang Zhang, Fei Yin, Yi-Han Hao, Cheng-Lin Liu

Geometry diagram parsing plays a key role in geometry problem solving, wherein the primitive extraction and relation parsing remain challenging due to the complex layout and between-primitive relationship.

Instance Segmentation Mathematical Question Answering +2

Unsupervised Structure-Texture Separation Network for Oracle Character Recognition

1 code implementation13 May 2022 Mei Wang, Weihong Deng, Cheng-Lin Liu

Second, transformation is achieved via swapping the learned textures across domains and a classifier for final classification is trained to predict the labels of the transformed scanned characters.

Disentanglement Unsupervised Domain Adaptation

Document Dewarping with Control Points

1 code implementation20 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.

Optical Character Recognition

Towards Open-Set Text Recognition via Label-to-Prototype Learning

no code implementations10 Mar 2022 Chang Liu, Chun Yang, Hai-Bo Qin, Xiaobin Zhu, Cheng-Lin Liu, Xu-Cheng Yin

In this paper, we introduce and formulate a new task, i. e., the open-set text recognition task, which demands the capability to spot and cognize novel characters without retraining.

Scene Text Recognition

Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks

no code implementations14 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.

Class-Incremental Learning via Dual Augmentation

1 code implementation 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.

class-incremental learning Incremental Learning

Delving into Feature Space: Improving Adversarial Robustness by Feature Spectral Regularization

no code implementations29 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.

Adversarial Robustness

Prototype Augmentation and Self-Supervision for Incremental Learning

no code implementations 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.

Incremental Learning Self-Supervised Learning

Semantic-Aware Video Text Detection

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.

Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network

1 code implementation14 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.

Misclassification Detection via Class Augmentation

no code implementations1 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.

Few-Shot Learning

Weakly-Supervised Arbitrary-Shaped Text Detection with Expectation-Maximization Algorithm

no code implementations1 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.

Computer Vision

Towards Robust Pattern Recognition: A Review

no code implementations12 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.

Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation

no code implementations CVPR 2019 Xiaobing Wang, Yingying Jiang, Zhenbo Luo, Cheng-Lin Liu, Hyun-Soo Choi, Sungjin Kim

Here, recurrent neural network based adaptive text region representation is proposed for text region refinement, where a pair of boundary points are predicted each time step until no new points are found.

Computer Vision Region Proposal +2

BlockQNN: Efficient Block-wise Neural Network Architecture Generation

2 code implementations16 Aug 2018 Zhao Zhong, Zichen Yang, Boyang Deng, Junjie Yan, Wei Wu, Jing Shao, Cheng-Lin Liu

The block-wise generation brings unique advantages: (1) it yields state-of-the-art results in comparison to the hand-crafted networks on image classification, particularly, the best network generated by BlockQNN achieves 2. 35% top-1 error rate on CIFAR-10.

Computer Vision Image Classification +1

SCAN: Sliding Convolutional Attention Network for Scene Text Recognition

no code implementations2 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.

Computer Vision Scene Text Recognition

Scene Text Recognition with Sliding Convolutional Character Models

no code implementations6 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.

Computer Vision Scene Text Recognition

Drawing and Recognizing Chinese Characters with Recurrent Neural Network

1 code implementation21 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.

Handwriting Recognition

Online and Offline Handwritten Chinese Character Recognition: A Comprehensive Study and New Benchmark

no code implementations18 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.

Benchmark Data Augmentation +1

Natural Scene Character Recognition Using Robust PCA and Sparse Representation

no code implementations15 Jun 2016 Zheng Zhang, Yong Xu, Cheng-Lin Liu

Natural scene character recognition is challenging due to the cluttered background, which is hard to separate from text.

Hybrid CNN and Dictionary-Based Models for Scene Recognition and Domain Adaptation

no code implementations29 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.

Domain Adaptation Scene Recognition

A Fast Projected Fixed-Point Algorithm for Large Graph Matching

1 code implementation3 Jul 2012 Yao Lu, Kai-Zhu Huang, Cheng-Lin Liu

In particular, with high accuracy, our algorithm takes only a few seconds (in a PC) to match two graphs of 1, 000 nodes.

Graph Matching

Robust Metric Learning by Smooth Optimization

no code implementations15 Mar 2012 Kaizhu Huang, Rong Jin, Zenglin Xu, Cheng-Lin Liu

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints.

Combinatorial Optimization Metric Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.