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arXiv 2025年

作者： Kareinen, Joona Skyttä, Annaliina Eerola, Tuomas Kraft, Kaisa Lensu, Lasse Suikkanen, Sanna Lehtiniemi, Maiju Kälviäinen, Heikki Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Finnish Environment Institute Helsinki Finland

This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an important role in marine ecosystems as primary producers and as a base of food webs. Given their sensitivity to environmental changes, fluctuations in plankton populations offer valuable information about oceans’ health and climate change motivating their monitoring. Modern automatic plankton imaging devices enable the collection of large-scale plankton image datasets, facilitating species-level analysis. Plankton species recognition can be seen as an image classification task and is typically solved using deep learning-based image recognition models. However, data collection in real aquatic environments results in imaging devices capturing a variety of non-plankton particles and plankton species not present in the training set. This creates a challenging fine-grained OSR problem, characterized by subtle differences between taxonomically close plankton species. We address this challenge by conducting extensive experiments on three OSR approaches using both phyto- and zooplankton images analyzing also on the effect of the rejection thresholds for OSR. The results demonstrate that high OSR accuracy can be obtained promoting the use of these methods in operational plankton research. We have made the data publicly available to the research community. © 2025, CC BY.

关键词： Biotic

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DFL: cross-view cross-layer discriminative feature learning for fine-grained 3D shape classification

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Neural Computing and Applications 2025年 1-22页

作者： Jiang, Jinzhe Bai, Jing Ma, Xiangyu The School of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images Processing and Pattern Recognition Laboratory North Minzu University Yinchuan China

Fine-grained 3D shape classification poses challenges in effectively capturing and integrating discriminative features residing in subtle local regions. Previous methods typically extract features independently from individual views of 3D shapes, with a focus on various strategies for fusing these extracted view features. However, this approach neglects interview correlations and potential redundancies among different views. In this study, we introduce $$\hbox {C}^2$$ DFL, which consists of two primary modules: cross-view discriminative feature extraction (CV-DFE) and cross-layer discriminative feature fusion (CL-DFF). CV-DFE integrates discriminative features by merging inputs from multiple views, mitigating limitations associated with isolated feature extraction. CL-DFF dynamically selects key tokens using a transformer model to interactively fuse discriminative features from various levels. Extensive experiments conducted on three categories of the FG3D dataset demonstrate the exceptional efficacy of $$\hbox {C}^2$$ DFL in capturing and integrating discriminative features of 3D shapes. The proposed method achieves state-of-the-art accuracy in fine-grained 3D shape classification (FGSC).

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Foreground Prediction for Image Composition with Local and Global Feature Fusion 16

Foreground Prediction for Image Composition with Local and G...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Sun, Liliang He, Yuanlie Li, Wensheng Feng, Fujian Liang, Yihui School of Computer Guangdong University of Technology Guangzhou510000 China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan528400 China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang550025 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

This paper focuses on the image composition of transparent objects, where existing image matting methods suffer from composition errors due to the lack of accurate foreground during the composition process. We propose a foreground prediction model named ALGM, which leverages the local feature extraction capabilities of Convolutional Neural Networks (CNNs) and incorporates an attention mechanism for global information modeling. The proposed alpha-assisted foreground prediction module extracts foreground information from the original image and conveys it. The extracted foreground color information is combined with the deep structural features of the encoder and used for foreground color prediction. ALGM reduces image composition errors in the quantitative data from the Composition-1k dataset and improves the visual quality of composed images on the AIM-500 and Transparent-460 datasets. © 2025 SPIE.

关键词： Prediction models

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FG3DFormer: Fine-Grained 3D Shape Classification Based on vision Transformer

FG3DFormer: Fine-Grained 3D Shape Classification Based on Vi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiangyu Ma Jing Bai Jinzhe Jiang Bin Peng The School of Computer Science and Engineering North Minzu University The Key Laboratory of Images Processing and Pattern Recognition Laboratory Yinchuan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Fine-grained 3D shape classification (FGSC) remains challenging due to the difficulty of adaptively capturing global structure differences and subtle inter-class distinctions. This paper directly extends vision Transformer (ViT) to FGSC, proposing a pure Transformer network FG3DFormer that fully leverages ViT’s global correlation and local attention abilities. FG3Dformer comprises the Hierarchical Feature Extraction (HFE) and the Hierarchical Feature Refinement (HFR), interconnected through the Adaptive View Region Selection (AVRS). Firstly, the HFE comprehensively evaluates the significance of intra-view patches and views driven by inter-view and intraview attention. Then, the AVRS adaptively selects crucial patch Tokens from different views to serve as sources of subtle local features. Finally, the HFR refines the 3D shape descriptor, capturing more discriminative global and subtle local features by leveraging both the view and selected crucial patch Tokens. Extensive experiments on FG3D and ModelNet40 demonstrate the superiority of FG3Dformer in FGSC and meta-category 3D shape classification tasks.

关键词： computer vision Visualization Solid modeling Three-dimensional displays Correlation Shape Signal processing Transformers Feature extraction Speech processing

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C2DFL: cross-view cross-layer discriminative feature learning for fine-grained 3D shape classification

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Neural Computing and Applications 2025年

作者： Jiang, Jinzhe Bai, Jing Ma, Xiangyu The School of Computer Science and Engineering North Minzu University Yinchuan750021 China The Key Laboratory of Images Processing and Pattern Recognition Laboratory North Minzu University Yinchuan750021 China

Fine-grained 3D shape classification poses challenges in effectively capturing and integrating discriminative features residing in subtle local regions. Previous methods typically extract features independently from individual views of 3D shapes, with a focus on various strategies for fusing these extracted view features. However, this approach neglects interview correlations and potential redundancies among different views. In this study, we introduce C2DFL, which consists of two primary modules: cross-view discriminative feature extraction (CV-DFE) and cross-layer discriminative feature fusion (CL-DFF). CV-DFE integrates discriminative features by merging inputs from multiple views, mitigating limitations associated with isolated feature extraction. CL-DFF dynamically selects key tokens using a transformer model to interactively fuse discriminative features from various levels. Extensive experiments conducted on three categories of the FG3D dataset demonstrate the exceptional efficacy of C2DFL in capturing and integrating discriminative features of 3D shapes. The proposed method achieves state-of-the-art accuracy in fine-grained 3D shape classification (FGSC). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： 3D shape classification Cross-layer feature fusion Cross-view attention Fine-grained classification Token selection Transformer

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OCR4HSV: A Multi-task Learning Approach for Handwritten Signature Verification 27th

OCR4HSV: A Multi-task Learning Approach for Handwritten Sig...

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27th International Conference on pattern recognition, ICPR 2024

作者： Lin, Chao-Qun Wang, Da-Han Su, Yan-Fei Ge, De-Wu Zhang, Xu-Yao School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Xiamen KEYTOP Communication Technology Co. Xiamen361024 China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9783031781186

Handwritten signature verification (HSV) models are notably recognized for their ability to discern whether a signature is forged in an offline document. Recently, HSV technology has made significant development. However, these methods have primarily focused on the shape features of the text image and overlooked the textual feature information inherent in the text itself, which makes the HSV model overfit. In this paper, we propose a novel network model named OCR4HSV to solve the above shortcomings. The proposed OCR4HSV first attempts to combine OCR and HSV to learn textual features in a multi-task learning manner. The model employs a dual-parameter approach, combining shared parameters and independent parameters. Specifically, Within the shared parameters, the Laplace attention module (LAM) is incorporated for edge information extraction. For independent parameters, CN-Mamba is utilized for sequence feature extraction in OCR, and the multi-scale global fusion block (MGFB) is designed to enhance the distinction between reference and test sample pairs. Leveraging OCR-related information and these architectural enhancements can fully mine the inherent textual feature information and significantly mitigate overfitting in the HSV task, thereby boosting verification accuracy. Our model has achieved state-of-the-art performance on the ChiSig and HanSig datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi-task learning

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SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive Learning for Efficient Cross-Emotion Anomaly Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China

Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different emotional modalities. To the best of our knowledge, this topic has yet to be well studied, which could potentially benefit lots of valuable cognitive applications such as autistic children diagnosis and criminal deception detection. To this end, this paper proposes an efficient cross-emotion anomaly detection approach via semanticinconsistency reasoning and hybrid contrastive learning (SIR-HCL), which is the first attempt to detect the anomalous emotional pairs across the audio-visual emotions. First, the proposed framework utilizes dual-branch network to obtain the deep emotional features in each modality, and then employs the shared residual block to derive the semantically compatible features. Subsequently, an efficient hybrid contrastive learning approach is designed to enlarge the semantic-inconsistency among abnormal emotional pair with different affective classes, while enhancing the semantic-consistency and increasing the feature correlation between normal emotional pair from the same affective class. At the same time, an efficient bidirectional learning scheme is employed to significantly improve the data utilization and a two-component Beta Mixture Model is adaptively utilized to reason the anomalous emotion pairs. Extensive experiments evaluated on two benchmark datasets show that the proposed SIR-HCL method can well detect the anomalous emotional pairs across audio-visual emotional data, and brings substantial improvements over the state-of-the-art competing methods. © 2016 IEEE.

关键词： Contrastive Learning

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CodePhys: Robust Video-Based Remote Physiological Measurement Through Latent Codebook Querying

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang Xi'an Jiaotong University School of Software Engineering Xi'an China Tencent Ai Lab Shenzhen China Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta53850 Finland University of Oulu Center for Machine Vision and Signal Analysis Finland

Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals. We consider noisy rPPG features as queries and generate high-fidelity rPPG features by matching them with noise-free PPG features from the codebook. Our approach also incorporates a spatial-aware encoder network with a spatial attention mechanism to highlight physiologically active areas and uses a distillation loss to reduce the influence of non-periodic visual interference. Experimental results on four benchmark datasets demonstrate that CodePhys outperforms state-of-the-art methods in both intra-dataset and cross-dataset settings. © 2025 IEEE.

关键词： Heart

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DLS-HCAN: Duplex Label Smoothing Based Hierarchical Context-Aware Network for Fine-grained 3D Shape Classification

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IEEE Transactions on Multimedia 2025年

作者： Bai, Shaojin Zheng, Liang Bai, Jing Ma, Xiangyu North Minzu University School of Computer Science and Engineering Yinchuan750021 China Liupanshan Laboratory Yinchuan750021 China North Minzu University Key Laboratory of Images Processing and Pattern Recognition LaboratoryCommission: IPPRLab Yinchuan750021 China

Fine-grained 3D shape classification (FGSC) has garnered significant attention recently and has made notable advancements. However, due to high inter-class similarity and intra-class diversity, it is still a challenge for existing methods to capture subtle differences between different subcategories for FGSC. On the one hand, one-hot labels in loss function are too hard to describe the above data characteristics, and on the other hand, local details are submerged in the global features extraction process and final network constraints, impacting classification results. In this paper, we propose a duplex label smoothingbased hierarchical context-aware network for fine-grained 3D shape classification, named DLS-HCAN. Specifically, DLS-HCAN firstly employs a hierarchical context-aware network (HCAN), in which the intra-view context attention mechanism (intra-ATT) and the inter-view context multilayer perceptron (inter-MLP) are designed to focus on and discern the beneficial local details. Subsequently, we propose a novel duplex label smoothing (DLS) regularization in which shape-level and view-level smooth labels are separately applied in two improved loss functions, adapting to the fine-grained data characteristics and considering the varying uniqueness of different views. Notably, our approach does not require additional annotation information. Experimental results and comparison with state-of-the-art methods demonstrate the superiority of our proposed DLS-HCAN for FGSC. In addition, our approach also achieves comparable performance for the coarse-grained dataset on ModelNet40. © 2025 IEEE.

关键词： Coarse-grained modeling

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CodePhys: Robust Video-based Remote Physiological Measurement through Latent Codebook Querying

arXiv

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arXiv 2025年

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang The School of Software Engineering Xi’an Jiaotong University Xi’an China The Tencent AI Lab Shenzhen China The Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology LUT Lappeenranta53850 Finland The Center for Machine Vision and Signal Analysis University of Oulu Finland

关键词： Heart

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