661 citations · 713 across the 19 of their papers we have counts for
19 papers
Arbitrary Shape Text Detection via Segmentation with Probability Maps
Shi-Xue Zhang, Xiaobin Zhu, Lei Chen +2
Arbitrary shape text detection is a challenging task due to the significantly varied sizes and aspect ratios, arbitrary orientations or shapes, inaccurate annotations, etc. Due to…
SD-GAN: Semantic Decomposition for Face Image Synthesis with Discrete Attribute
Zhou Kangneng, Zhu Xiaobin, Gao Daiheng +3
Manipulating latent code in generative adversarial networks (GANs) for facial image synthesis mainly focuses on continuous attribute synthesis (e.g., age, pose and emotion), while…
Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization
Mengyin Liu, Chao Zhu, Hongyu Gao +4
With the prosperity of e-commerce industry, various modalities, e.g., vision and language, are utilized to describe product items. It is an enormous challenge to understand such di…
Arbitrary Shape Text Detection via Boundary Transformer
Shi-Xue Zhang, Chun Yang, Xiaobin Zhu +1
In arbitrary shape text detection, locating accurate text boundaries is challenging and non-trivial. Existing methods often suffer from indirect text boundary modeling or complex p…
Graph Fusion Network for Multi-Oriented Object Detection
Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou +1
In object detection, non-maximum suppression (NMS) methods are extensively adopted to remove horizontal duplicates of detected dense boxes for generating final object instances. Ho…
Open-set Text Recognition via Character-Context Decoupling
Chang Liu, Chun Yang, Xu-Cheng Yin
The open-set text recognition task is an emerging challenge that requires an extra capability to cognize novel characters during evaluation. We argue that a major cause of the limi…
Kernel Proposal Network for Arbitrary Shape Text Detection
Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou +2
Segmentation-based methods have achieved great success for arbitrary shape text detection. However, separating neighboring text instances is still one of the most challenging probl…
Towards Open-Set Text Recognition via Label-to-Prototype Learning
Chang Liu, Chun Yang, Hai-Bo Qin +3
Scene text recognition is a popular topic and extensively used in the industry. Although many methods have achieved satisfactory performance for the close-set text recognition chal…
Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification
Zhiyu Fang, Xiaobin Zhu, Chun Yang +3
Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC). However, due…
Non-autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition
Chuan-Fei Zhang, Yan Liu, Tian-Hao Zhang +3
Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and a substantial part of NAR transformer models is to use the casu…
Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection
Shi-Xue Zhang, Xiaobin Zhu, Chun Yang +2
Arbitrary shape text detection is a challenging task due to the high complexity and variety of scene texts. In this work, we propose a novel adaptive boundary proposal network for…
End-to-end trainable network for degraded license plate detection via vehicle-plate relation mining
Song-Lu Chen, Shu Tian, Jia-Wei Ma +4
License plate detection is the first and essential step of the license plate recognition system and is still challenging in real applications, such as on-road scenarios. In particu…
RUArt: A Novel Text-Centered Solution for Text-Based Visual Question Answering
Zan-Xia Jin, Heran Wu, Chun Yang +4
Text-based visual question answering (VQA) requires to read and understand text in an image to correctly answer a given question. However, most current methods simply add optical c…
Mutual-Supervised Feature Modulation Network for Occluded Pedestrian Detection
Ye He, Chao Zhu, Xu-Cheng Yin
State-of-the-art pedestrian detectors have achieved significant progress on non-occluded pedestrians, yet they are still struggling under heavy occlusions. The recent occlusion han…
Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection
Shi-Xue Zhang, Xiaobin Zhu, Jie-Bo Hou +4
Arbitrary shape text detection is a challenging task due to the high variety and complexity of scenes texts. In this paper, we propose a novel unified relational reasoning graph ne…
Semantic Bilinear Pooling for Fine-Grained Recognition
Xinjie Li, Chun Yang, Songlu Chen +2
Naturally, fine-grained recognition, e.g., vehicle identification or bird classification, has specific hierarchical labels, where fine categories are always harder to be classified…
AdaDNNs: Adaptive Ensemble of Deep Neural Networks for Scene Text Recognition
Chun Yang, Xu-Cheng Yin, Zejun Li +4
Recognizing text in the wild is a really challenging task because of complex backgrounds, various illuminations and diverse distortions, even with deep neural networks (convolution…
Learning to Diversify via Weighted Kernels for Classifier Ensemble
Xu-Cheng Yin, Chun Yang, Hong-Wei Hao
Classifier ensemble generally should combine diverse component classifiers. However, it is difficult to give a definitive connection between diversity measure and ensemble accuracy…
Robust Text Detection in Natural Scene Images
Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang +1
Text detection in natural scene images is an important prerequisite for many content-based image analysis tasks. In this paper, we propose an accurate and robust method for detecti…