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7th Chinese Conference on pattern recognition and Computer Vision

作者： Chen, Yu Wu, Jiahua Wang, Da-Han Zhang, Xinxin Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China

ISBN: (纸本)9789819784981;9789819784998

Accurate medical image segmentation of lesion areas is crucial in assisting diagnostics and treatment planning of diseases. In this paper, we propose a boundary-guided context fusion U-Net(BCF-UNet) for medical image segmentation to address a significant problem with existing approaches: ignoring the relationships between the boundary information and semantic context. In the BCF-UNet, we propose a novel feature fusion module, named Semantic Context-based Adaptive Fusion Module (SCAFM), to incorporate the different levels of encoder outputs and transfer them to the decoder. The SCAFM allows our network to fuse the deepest semantic information with the shallow edge information to greatly bridge the gap between different scale features. Then, in each layer of the encoder sub-network, we propose a newly pyramid edge enhancement module with edge-related prior knowledge to obtain the edge multi-granularity information, which enhances the accuracy of the segmentation boundary. In the BCF-UNet, edge information is peculiarly utilized to enhance the capability of semantic context extraction, which helps the model better understand and recognize the relationship between different tissue structures. The result of extensive experiments on two datasets shows that the proposed BCF-UNet outperforms stateof-art techniques.

关键词： Medical image Segmentation center dot Adaptive Feature Fusion center dot Edge Enhancement center dot U-Net

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CCNN-former: Combining convolutional neural network and Transformer for image-based traffic time series prediction

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Liu, Lijuan Wu, Mingxiao Lv, Qinzhi Liu, Hang Wang, Yan Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understand Xiamen 361024 Peoples R China

Traffic time series prediction is crucial to the development of urban intelligent transportation systems (ITS). Traditional prediction models are mainly designed to extract the spatio-temporal features based on historical traffic flow in the form of time series, ignoring the learning of the implicit periodic patterns hidden in the flow. In addition, most of the models solely employ the historical traffic flow with no more than 12 time steps for prediction. Such short-term input struggles to contain rich, continuous and diverse periodic patterns, limiting their performance. To this end, we propose a model of combining convolutional neural network (CNN) and Transformer (CCNN-Former) tailored for image-based traffic time series prediction. Specifically, we first propose a data conversion module that converts the long-term time series traffic flow into a new representation of image with fixed resolution, ensuring that both short- and long-term information is contained in an image. Then, CNN and Transformer are utilized to learn the image-based features from local and global perspectives, respectively. The core of CCNN-Former is to creatively propose a new long-term time series data imaging strategy and use a computer vision method to extract rich short- and long-term image-based features, achieving the goal of improving prediction performance while maintaining high efficiency. Extensive experiments are conducted on two real-world datasets to demonstrate the superiority of CCNN-Former compared with seven competitive baselines.

关键词： Traffic prediction Time series Imaging Periodic pattern Transformer

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Multimodal urban traffic flow prediction based on multi-scale time series imaging

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pattern recognitION 2025年 164卷

作者： Lv, Qinzhi Liu, Lijuan Yang, Ruotong Wang, Yan Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understand Xiamen 361024 Peoples R China

Accurate traffic flow prediction is of great significance for administrators and travelers to make informed decisions in advance. Since the increasing correlation between predicted flow and historical flow from more recent periods, most existing traffic flow prediction models generally learn spatial-temporal patterns from historical single-scale time series data with time steps not exceeding 12. However, such short-term flow does not contain continuous and dynamic long-term spatial-temporal patterns. Thus, how to comprehensively learn the diverse patterns and achieve a satisfactory balance between effectiveness and efficiency presents a challenge. To this end, we propose a multimodal urban traffic flow prediction model based on multi-scale time series imaging (MM-TSI). Specifically, a data processing mechanism of multi-scale time series imaging is specially designed to efficiently learn both short- and long-term spatial-temporal patterns. After that, an image- based module is proposed to be parallelly integrated into a traditional time series-based module. By adaptively fusing the two-modal features extracted from image-based module and time series-based module, MM-TSI is capable of effectively learning more comprehensive and diverse spatial-temporal patterns while maintaining efficiency to a certain extent. Extensive experiments are conducted on three real-world urban traffic flow datasets. The results demonstrate that the proposed MM-TSI significantly outperforms the state-of-the-art (SOTA) models and exhibits generalization ability in both short- and long-term prediction.

关键词： Urban traffic flow prediction Multimodal learning Time series Imaging Multi-scale modeling

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Joint radical embedding and detection for zero-shot Chinese character recognition

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pattern recognitION 2025年 161卷

作者： Luo, Guo-Feng Wang, Da-Han Zhang, Xu-Yao Lin, Zi-Hao Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Fujian Key Lab Pattern Recognit & Image Understand Xiamen Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

Radical-based Zero-shot Chinese character recognition (ZSCCR) has attracted much attentions in recent years as radicals are common mid-level primitives that can bridge seen and unseen classes. However, while existing radical detection and sequence matching based methods have the difficulty of training a high-performance radical detection model due to the lacking of positional information of radicals in the character images, the attribute embedding based methods directly drop the radical detection procedure and overlooked the rich structural and spatial information of radicals in the visual perspective. In this paper, we proposed a novel joint radical embedding and detection method (JRED) for ZSCCR, which integrates the radical detection into the radical-level attribute embedding to explore and utilize the radicals' structural and spatial information for ZSCCR. Specifically, we maintain a learnable embedding vector for representing each radical and learn their representations without the positional information of radicals. The learned radical representations are regarded as the radical detectors to obtain the radical-level attributes by sliding the detector on the visual feature maps. The attributes are then used for the character representation by attribute embedding. Comprehensive experiments on various datasets show that the proposed JRED method can achieve the state-of-the-art performance, demonstrating the effectiveness of our proposed approach.

关键词： Zero-shot learning Chinese character recognition Joint radical embedding and detection

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Low-light image enhancement with quality-oriented pseudo labels via semi-supervised contrastive learning

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 276卷

作者： Jiang, Nanfeng Cao, Yiwen Zhang, Xu-Yao Wang, Da-Han He, Yifan Wang, Chiming Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Fujian Key Lab Pattern Recognit & Image Understand Xiamen Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Reconova Technol Co Ltd Xiamen Key Lab Visual Percept Technol & Applicat Xiamen Peoples R China

Existing Low-Light image Enhancement (LLIE) methods exhibit limitations when applied to complex scenes, primarily due to the lack of adaptation to real-world and synthetic images. To address this issue, we introduce a novel approach that leverages the mean-teacher strategy and contrastive regularization within a Contrastive Semi-Learning Network (CSL-Net). Specially, our CSL-Net employs high-quality pseudo labels to significantly improve model robustness and generalization. The novelties of this paper lie in: (1) We utilize a score- pool to store teacher's and student's outputs during training. After that, we propose a monotonic strategy to determine the most reliable Non-Reference image Quality Assessment (NR-IQA) metric, which provides effective evaluations and determines the optimal output as the pseudo-label. (2) We integrate this selected pseudo-label with a contrastive loss to provide auxiliary information that mitigates overfitting issues and guides student towards generating realistic images. (3) In network design, we incorporate illumination and gradient priors to refine color appearance and detail preservation, facilitated by several elaborated modules, including Contextual Refinement Blocks (CRBs), Non-Local Dynamic Attention (NLDA) and Illumination Perceptual Enhancer (IPE). Experimental results on popular datasets demonstrate that the enhancement quality can be significantly improved by our proposed method.

关键词： Low-light image enhancement Semi-supervised learning Contrastive learning

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DHLA: Dynamic Hybrid label Assignment for End-to-End Object Detection

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第2期35卷 1055-1069页

作者： Hu, Zhiliang Chen, Si Hua, Yang Wang, Da-Han Zhu, Shunzhi Yan, Yan Xiamen Univ Technol Sch Comp & Informat Engn Fujian Key Lab Pattern Recognit & Image Understand Xiamen 361024 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Queens Univ Belfast Inst Elect Commun & Informat Technol Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN North Ireland Xiamen Univ Sch Informat Xiamen 361005 Peoples R China

The recent one-to-one label assignment plays a crucial role in removing the last non-differentiable component, i.e., Non-Maximum Suppression (NMS), used in the post-processing step of the one-to-many label assignment, thus building an efficient end-to-end detection system. However, due to the limited number of foreground samples, the one-to-one label assignment often suffers from insufficient representation learning, and its performance is inferior to that of traditional detectors trained using the one-to-many label assignment. To solve these problems, we introduce a novel Dynamic Hybrid label Assignment (DHLA) method, including a Hybrid Sample Selection (HSS) strategy and a Stage-aware Soft-label Adjustment (SSA) mechanism. In order to enhance the ability of representation learning of the one-to-one label assignment, the HSS strategy subtly integrates the one-to-many and the one-to-one label assignment rules to form a simple and effective hybrid assignment rule, where high-quality samples are selected for training according to an effective task consistency metric. Moreover, the SSA mechanism dynamically adjusts the contributions of different foreground samples at different training stages, thus effectively achieving the transition from one-to-many to one-to-one label assignment. In addition, we leverage a ranking loss function to widen the score gaps between the highest scoring position and surrounding areas for effectively removing duplicate bounding boxes. As a result, our method not only learns robust feature representations during training but also performs efficient end-to-end detection during inference. Extensive experiments demonstrate our method achieves competitive performance compared to state-of-the-art detectors on the challenging COCO and CrowdHuman datasets.

关键词： Training Detectors Object detection Feature extraction Circuits and systems Transformers Silicon Representation learning Measurement DH-HEMTs NMS-free label assignment dynamic soft label

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Stain-adaptive self-supervised learning for histopathology image analysis

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pattern recognitION 2025年 161卷

作者： Ye, Haili Yang, Yuan-yuan Zhu, Shunzhi Wang, Da-Han Zhang, Xu-Yao Yang, Xin Huang, Heguang Xiamen Univ Technol Sch Comp & Informat Engn Fujian Key Lab Pattern Recognit & Image Understand Xiamen 361024 Fujian Peoples R China Fujian Med Univ Union Hosp Dept Gen Surg 29 Xinquan Rd Fuzhou 350001 Fujian Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430000 Hubei Peoples R China

Staining variability is a critical factor affecting the accuracy of histopathological image analysis by reducing the distinguishability of tissue regions. Existing methods employ preprocessing techniques such as color matching and stain transfer for stain normalization, which can compromise data features. We propose a novel Stain-Adaptive Self-Supervised Learning (SASSL) method for histopathological image analysis. Our SASSL integrates a stain domain adversarial training module into the self-supervised learning (SSL) framework, allowing adaptation to staining variations while learning invariant features. SASSL can be viewed as a general invariant representation SSL method, with derived self-supervised weights applicable to various downstream tasks (classification, regression, and segmentation) in histopathological images. We conducted experiments on publicly available histopathological image analysis datasets, including PANDA, BreastPathQ, and CAMELYON16, achieving state-of-the-art performance. Results demonstrate that SASSL enhances feature extraction and mitigates the impact of staining variability, consistently improving performance across tasks. Our code is available at https://***/YeahHighly/SASSL_PR_2024.

关键词： Histopathology image analysis Domain adaptation Stain adaptation Self-supervised learning Domain adversarial training

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Few-labeled visual recognition for self-driving using multi-view visual-semantic representation

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NEUROCOMPUTING 2021年 428卷 361-367页

作者： Wang, Da-Han Li, Jianmin Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Fujian Key Lab Pattern Recognit & Image Understan 600 Ligong Rd Xiamen 361024 Fujian Peoples R China

For a car self-driving system, it is vital to accurately recognize the objects on the road. This is often achieved by analyzing the images captured by various cameras. However, in real applications, we often have limited number of labeled images. Besides, it is very expensive to manually annotate objects in images. It is hard to learn reliable classifiers when only few-labeled images are available. To alleviate these problems, multi-view information is used. However, only using visual information is not enough for reliable classification. Besides, we often have limited images with labels. To cope with these problems, in this paper, we propose a novel multi-view visual-semantic representation method for few-labeled visual recognition ((MVS)-S-2). We make use of the state-of-the-art deep convolutional neural networks by viewing them as different views to extract semantic representations of images. This is achieved by using the learned deep convolutional neural networks to make predictions of the semantics of images. Both the visual and semantic representations of images are then used to predict the categories of images by combining the predictions of multi-views with visual and semantic consistency constraints. Experiments on four public available datasets prove the effectiveness of the proposed MV2S method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Few-labeled view recognition Self-driving object categorization Multi-view learning Semantic representation

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Sampling Consensus by Neighborhood Interaction Information for Remote Sensing image Matching

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Guo, Hanlin Chen, Weimin Deng, Zhao Chen, Si Hong, Xin Chen, Shuting Wang, Da-Han Xiamen Univ Technol Sch Elect Engn & Automat Xiamen 361024 Peoples R China Xiamen Key Lab Frontier Elect Power Equipment & In Xiamen 361024 Peoples R China Xiamen Univ Technol Sch Optoelect & Commun Engn Xiamen 361024 Peoples R China Xiamen Univ Technol Sch Comp & Informat Engn Fujian Key Lab Pattern Recognit & Image Understand Xiamen 361024 Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Jimei Univ Coll Chengyi Xiamen 361021 Peoples R China

In remote sensing and photogrammetry, establishing reliable feature correspondences between two sets of feature points is a critical preprocessing step. In this letter, we propose a novel outlier removal method, termed sampling consensus by neighborhood interaction information (SACNI), to accurately distinguish true matches (i.e., inliers) from false matches (i.e., outliers) for remote sensing image matching. Inspired by social networks, where two individuals with close ties share numerous common connections, we propose a neighborhood interactive representation (NIR) strategy to effectively evaluate correlations among feature matches, thereby guiding the efficient sampling of outlier-free data subsets. This representation is integrated into both the initial data subset selection and optimization phases, enhancing the overall matching performance. Extensive experiments on challenging remote sensing datasets show the superiority of the proposed SACNI over several other state-of-the-art methods.

关键词： Remote sensing Data models image matching Computational modeling Feature extraction Estimation Correlation Optimization Accuracy Sensors Guided sampling neighborhood interactions outlier removal robust feature matching

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Deep Spatial-Temporal Network Based on Residual Networks and Dilated Convolution for Traffic Flow Prediction 7

Deep Spatial-Temporal Network Based on Residual Networks and...

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IEEE 7th International Conference on Intelligent Transportation Engineering (ICITE)

作者： Dong, Liangming Zhang, Xinxin Liu, Lijuan Xiamen Univ Technol Coll Comp & Informat Engn Fujian Key Lab Pattern Recognit & Image Understan Xiamen Peoples R China

ISBN: (纸本)9781665460071

As modern cities become more intelligent, traffic flow forecasting is becoming increasingly significant in intelligent transport and urban governance. Accurate traffic forecasting can help the management and the residents to travel, and how to achieve accurate and efficient traffic forecasting is a problem that has been studied by many scholars. With the growth of artificial intelligence and the generation of large-scale traffic data, deep learning can be more effective than traditional probabilistic prediction models in traffic flow forecasting, but there are two more problems to be solved. Firstly, the use of too many levels of convolution often leads to over-fitting. Secondly, the state of traffic in a city can be impacted by external factors such as meteorological conditions, temperature, and wind speed. To solve this problem, we propose an end-to-end neural network model based on the residual network, dilated convolution, and call it STDCN. Convolution and dilated convolution are used in the model to obtain local spatial dependencies and distant spatial dependencies. We designed an external feature extractor to transform the external factors into a 1D tensor using the One-Hot Encoding.

关键词： traffic flow forecasts intelligent transport deep learning external factors dilated convolutional

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