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Learning Explicit Radical Representations for Zero-Shot Chinese Character recognition 27th

Learning Explicit Radical Representations for Zero-Shot Chin...

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27th International Conference on pattern recognition, ICPR 2024

作者： Pan, Song-Liang Wang, Da-Han Jiang, Nanfeng Zhang, Xu-Yao Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Zero-shot Chinese character recognition (ZSCCR) aims to recognize unseen Chinese characters by learning the semantic knowledge of seen characters. Radical-based methods treat Chinese characters as combinations of radicals, recognizing characters by predicting the radicals in the images. Existing radical-based methods have a closed radical parsing process that cannot be intervened in mid-course, relying only on semantic labels for constraints. However, semantic embedding vectors are usually manually designed and lack alignment with visual features, making it extremely difficult for the model to learn and locate discriminative radical representations from visual features. This paper proposes a ZSCCR network called Learning Explicit Radical Representations (LERRNet). LERRNet introduces learnable attribute hint vectors to guide the model in locating discriminative radicals and learning explicit representations of images. Specifically, we introduce a Radical Relevance Enhanced Encoder (RREE) to enhance the correlation of local radicals by augmenting the relationships between grid regions in visual features. Guided by attribute hint vectors, LERRNet employs a Radical Representation Decoder (RRD) to locate the most relevant regions of each radical in the given image and learn explicit radical representations. Extensive experiments demonstrate that LERRNet outperforms state-of-the-art radical/stroke-based methods across three ZSCCR benchmarks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Zero-shot learning

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Deform-CAM: Self-attention Based on Deformable Convolution for Weakly Supervised Semantic Segmentation 17th

Deform-CAM: Self-attention Based on Deformable Convolution f...

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17th Chinese Conference on image and Graphics Technologies and Applications, IGTA 2022

作者： Huang, Feihong Wang, Da-Han Ye, Hai-Li Zhu, Shunzhi School of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9789811950957

Weakly-supervised semantic segmentation (WSSS) receives increasing attentions from the community in recent years as it leverages the weakly annotated data to solve the problem of lacking of fully annotated data. Among them, the WSSS method based on image-level annotation is the most direct and effective while the image-level annotation is easy to obtain. Most advanced methods use class activation maps (CAM) as initial pseudo-labels, however, they only identify local regions of the target, while ignoring the context information among local regions. To solve this problem, this paper proposes a deformable convolution based self-attention module (DSAM), which introduces a pixel relationship matrix, to learn the contextual information of the image. A regularization loss is introduced to narrow the distance between the DSAM and the CAM. Compared to the base CAM method, our method can identify more target features and robustly improve the performance of WSSS without training the classifier multiple times. Our proposed method achieves the mIoU of 65.5% and 66.8% on the Pascal VOC 2012 val and test sets, respectively, demonstrating the feasibility of the method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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ResAsapp: An Effective Convolution to Distinguish Adjacent Pixels For Scene Text Detection 22

ResAsapp: An Effective Convolution to Distinguish Adjacent P...

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Proceedings of the 2022 11th International Conference on Computing and pattern recognition

作者： Kangming Weng Xia Du Kunze Chen Dahan Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

The segmentation-based approach is an essential direction of scene text detection, and it can detect arbitrary or curved text, which has attracted the increasing attention of many researchers. However, extensive research has shown that the segmentation-based method will be disturbed by adjoining pixels and cannot effectively identify the text boundaries. To tackle this problem, we proposed a ResAsapp Conv based on the PSE algorithm. This convolution structure can provide different scale visual fields about the object and make it effectively recognize the boundary of texts. The method's effectiveness is validated on three benchmark datasets, CTW1500, Total-Text, and ICDAR2015 datasets. In particular, on the CTW1500 dataset, a dataset full of long curve text in all kinds of scenes, which is hard to distinguish, our network achieves an F-measure of 81.2%.

关键词： Pixel Interference Scene Text Detection Curved Text

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Combining Attention Mechanism and Local-Global Features Association Network for Vehicle Re-identification 22

Combining Attention Mechanism and Local-Global Features Asso...

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Proceedings of the 2022 11th International Conference on Computing and pattern recognition

作者： Cai-Xia Li Xia Du Yun Wu Da-Han Wang Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Vehicle re-identification (Re-ID) aims to retrieve the target vehicle from a large dataset composed of vehicle images captured by multiple cameras. Most vehicles are difficult to recognize in the environment of low resolution, occlusion, and viewpoint change, which brings challenges to vehicle Re-ID. Existing work usually uses additional attribute information to distinguish different vehicles, such as color, viewpoint, and model. However, this requires expensive manual annotation. Therefore, we propose a three-branch network based on attention mechanism and local-global feature association (AM-LGFA) to improve the accuracy of vehicle Re-ID. In the global branch, the global features of the vehicle are extracted. A multi-scale channel attention module is introduced into the attention branch to suppress irrelevant information and extract important channel features. The features extracted from the backbone are divided into different stripe features in the horizontal direction in the local branch. Then connect each stripe feature with the global information to enhance the context between features. Finally, the features extracted from the three branches are concatenated as the feature representation of the test phase. The experimental results show that the features extracted by the AM-LGFA network are complementary. The effectiveness of this method is verified on two challenging public datasets, VehicleID and VeRi-776.

关键词： Attention mechanism Vehicle re-identification Local-global feature association

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A Lightweight Brain Tumor Segmentation Network Based on 3D Inverted Residual Modules 22

A Lightweight Brain Tumor Segmentation Network Based on 3D I...

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Proceedings of the 2022 11th International Conference on Computing and pattern recognition

作者： Yuchao Liu Xia Du Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Semantic segmentation technology based on deep learning has played an important role for doctors in identifying brain tumor regions and formulating treatment plans. Popular automated segmentation methods for brain tumors include 2D and 3D convolution networks. The 3D networks give better results but lead to a significant increase in parameters and computational cost. In this paper, we propose a lightweight brain tumor segmentation network composed of 3D inverted residual modules, which can significantly reduce the computational complexity of 3D models. Based on a lightweight depthwise separable convolution, our 3D inverted residual module extracts high-dimensional brain tumor features through an intermediate expansion layer, thus improving performance. On the brain tumor dataset BraTS 2018, our network achieves dice scores of 80.8%, 90.7%, and 84.3% (for ET, WT, and TC, respectively) with only 0.68M parameters and 51.46G FLOPs. The results show that our method can significantly reduce the complexity of the 3D model and achieve very competitive performance.

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Swin Transformer with Multi-Scale Residual Attention for Semantic Segmentation of Remote Sensing images 22

Swin Transformer with Multi-Scale Residual Attention for Sem...

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Proceedings of the 2022 11th International Conference on Computing and pattern recognition

作者： Yuanyang Lin Da-han Wang Yun Wu Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Semantic segmentation of remote sensing images usually faces the problems of unbalanced foreground-background, large variation of object scales, and significant similarity of different classes. The FCN-based fully convolutional encoder-decoder architecture seems to have become the standard for semantic segmentation, and this architecture is also prevalent in remote sensing images. However, because of the limitations of CNN, the encoder cannot obtain global contextual information, which is extraordinarily important to the semantic segmentation of remote sensing images. By contrast, in this paper, the CNN-based encoder is replaced by Swin Transformer to obtain rich global contextual information. Besides, for the CNN-based decoder, we propose a multi-level connection module (MLCM) to fuse high-level and low-level semantic information to help feature maps obtain more semantic information and use a multi-scale upsample module (MSUM) to join the upsampling process to recover the resolution of images better to get segmentation results preferably. The experimental results on the ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of our proposed method.

关键词： Dilated Convolution Semantic Segmentation Remote Sensing Swin Transformer

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Multi-Scale Channel Attention for Chinese Scene Text recognition 22

Multi-Scale Channel Attention for Chinese Scene Text Recogni...

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Proceedings of the 2022 11th International Conference on Computing and pattern recognition

作者： Haiqing Liao Xia Du Yun Wu Da-Han Wang Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Scene text recognition have proven to be highly effective in solving various computer vision tasks. Recently, numerous recognition algorithms based on the encoder-decoder framework have been proposed for handling scene texts with perspective distortion and curve shape. Nevertheless, most of these methods only consider single-scale features while not taking multi-scale features into account. Meanwhile, the existing text recognition methods are mainly used for English texts, whereas ignoring Chinese texts' pivotal role. In this paper, we proposed an end-to-end method to integrate multi-scale features for Chinese scene text recognition (CSTR). Specifically, we adopted and customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture multi-scale features of the input image while simultaneously extending the receptive fields. Moreover, we added Squeeze-and-Excitation Networks (SE) to capture attentional features with global information to improve the performance of CSTR further. The experimental results of the Chinese scene text datasets demonstrate that the proposed method can efficiently mitigate the impacts of the loss of contextual information caused by the text scale varying and outperforms the state-of-the-art approaches.

关键词： Chinese Scene Text recognition Multi-scale Dense Atrous Spatial Pyramid Pooling

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Multi-Scale Features Integration for Handwritten Mathematical Expression recognition 22

Multi-Scale Features Integration for Handwritten Mathematica...

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Proceedings of the 2022 11th International Conference on Computing and pattern recognition

作者： Xianghao Liu Da-Han Wang Shunzhi Zhu Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China

ISBN: (纸本)9781450397056

Handwritten mathematical expression recognition (HMER) is a challenging task due to the complex two-dimensional structure of mathematical expressions and the similarity of handwritten texts. Most existing methods for HMER only consider single-scale features while ignoring multi-scale features that are very important to HMER. Few works have explored the fusion of multi-scale features in HMER, but exhibited an extra branch that brings more parameters and computation. In this paper, we propose an end-to-end method to integrate multi-scale features using a unified model. Specifically, we customized the Dense Atrous Spatial Pyramid Pooling (DenseASPP) to our backbone network to capture the multi-scale features of the input image meanwhile expanding the receptive fields. Moreover, we added a symbol classifier using focal loss to better discriminate and recognize similar symbols, to further improve the performance of HMER. Experiments on the Competition on recognition of Online Handwritten Mathematical Expressions (CROHME) 2014, 2016 and 2019 shows that the proposed method achieves superior performance to most state-of-the-art methods, demonstrating the effectiveness of the proposed method.

关键词： Multi-Scale Handwritten Mathematical Expression recognition, Dense Atrous Spatial Pyramid Pooling, Symbol Classifier

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Exploring Joint Information of Multi-Scales for Vehicle Re-Identification 22

Exploring Joint Information of Multi-Scales for Vehicle Re-I...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Zhou, Yongjie Wang, Dahan School of Computer and Information Engineering Xiamen University of Technology Xiamen Chinese Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Vehicle re-identification (re-ID) is an essential component of intelligent video surveillance, which attempts to solve the problem of retrieving specific vehicle instances. The technical challenge is mainly the requirement that the algorithm be robust under different viewing angles, resolutions, occlusions, and lighting conditions, yet different conditions can arise where the appearance of the exact vehicle varies dramatically. In contrast, the appearance of two different vehicles is exceptionally similar. To overcome this enormous challenge: we adopt a novel multi-scales attention network architecture, which will feature representation for feature maps at different scales to enhance the recognition of intricate parts, and introduce an inter-coordinate attention module to build interdependencies between component positions, making it possible to build a more robust vehicle model feature representation, and finally this paper also proposes a camera-based vehicle re localization method that enables the model to calculate the vehicle occurrence probability using the potential information among road network cameras as a post-processing step to re-correct the confidence level of the re-identification ranking. Experimental results show that the method outperforms the reference method in all indexes even without post-processing. At the same time, the camera confidence correction can be used in overlay with the reordering clustering correction method, which can improve the results by four percentage points and MAP up to 81.4%. © 2022 ACM.

关键词： Vehicles

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CDE-Net: A semi-supervised nucleus instance segmentation method based on center deviation estimation 22

CDE-Net: A semi-supervised nucleus instance segmentation met...

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Yan, Yi Wang, Da-Han Ye, Haili Zhu, Shunzhi Li, Jianmin School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Cell nucleus segmentation is an important step in the diagnosis of tumor malignancy. In the case of out-of-focus, the cell nucleus boundary tends to be blurred, which increases the difficulty of cell segmentation and marking. Therefore, in the case of marking only some core instances, it is very important to realize the segmentation of core instances. In this paper, we propose a semi-supervised nucleus instance segmentation method based on anchor bias prediction that is achieved by our proposed center deviation estimation network (CDE-Net). By introducing the CDE branch to predict the offset degree of the anchor frame, and weighting the confidence of the anchor frame, more accurate prediction results can be obtained. The model adopts the semi-supervised training method to progressively learn the CDE-Net model, which alleviates the problem of imbalance between positive and negative samples caused by only marking part of the nuclear samples. The experimental results show that CDE-Net is superior to Mask R-CNN in many evaluation indicators, and achieves SOTA on the partially labelled MoNuSeg data set. © 2022 Association for Computing Machinery. All rights reserved.

关键词： Diagnosis

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