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Visual Prompt Flexible-Modal Face Anti-Spoofing

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2597-2606页

作者： Yu, Zitong Cai, Rizhao Cui, Yawen Liu, Ajian Chen, Changsheng Great Bay University School of Computing and Information Technology Dongguan523000 China Nanyang Technological University ROSE Lab School of EEE 639798 Singapore Hong Kong Polytechnic University Kowloon Hong Kong Chinese Academy of Sciences University of Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Beijing100190 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security College of Electronics and Information Engineering Shenzhen518060 China

Recently, vision transformer based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, multimodal face data collected from the real world is often imperfect due to missing modalities from various imaging sensors. Recently, flexible-modal FAS (Yu et al. 2023) has attracted more attention, which aims to develop a unified multimodal FAS model using complete multimodal face data but is insensitive to test-time missing modalities. In this paper, we tackle one main challenge in flexible-modal FAS, i.e., when missing modality occurs either during training or testing in real-world situations. Inspired by the recent success of the prompt learning in language models, we propose Visual Prompt flexible-modal FAS (VP-FAS), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to downstream flexible-modal FAS task. Specifically, both vanilla visual prompts and residual contextual prompts are plugged into multimodal transformers to handle general missing-modality cases, while only requiring less than 4% learnable parameters compared to training the entire model. Furthermore, missing-modality regularization is proposed to force models to learn consistent multimodal feature embeddings when missing partial modalities. Extensive experiments conducted on two multimodal FAS benchmark datasets demonstrate the effectiveness of our VP-FAS framework that improves the performance under various missing-modality cases while alleviating the requirement of heavy model re-training. © 2004-2012 IEEE.

关键词： Alignment

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th International Conference on Bioinformatics and intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

Patterns

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patterns 2025年

作者： Li, Yi-Fan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs. © 2025 The Author(s)

关键词： Deep learning

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Efficient Image Super-Resolution With Feature Interaction Weighted Hybrid Network

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IEEE Transactions on Multimedia 2025年 27卷 2256-2267页

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Yang, Jian Qi, Guo-Jun Lin, Chia-Wen Beijing University of Posts and Telecommunications Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing100080 China Shanghai University School of Communication and Information Engineering Shanghai200444 China Nanjing University of Posts and Telecommunications IVIPLab Institute of Advanced Technology Nanjing210046 China Ministry of Education Key Laboratory of Artificial Intelligence Shanghai200240 China Soochow University Provincial Key Laboratory for Computer Information Processing Technology Suzhou215006 China Nanjing University of Science and Technology School of Computer Science and Technology Nanjing210094 China Westlake University Research Center for Industries of the Future School of Engineering Hangzhou310024 China OPPO Research SeattleWA98101 United States National Tsing Hua University Department of Electrical Engineering Institute of Communications Engneering Hsinchu300044 Taiwan

Lightweight image super-resolution aims to reconstruct high-resolution images from low-resolution images using low computational costs. However, existing methods result in the loss of middle-layer features due to activation functions. To minimize the impact of intermediate feature loss on reconstruction quality, we propose a Feature Interaction Weighted Hybrid Network (FIWHN), which comprises a series of Wide-residual Distillation Interaction Block (WDIB) as the backbone. Every third WDIB forms a Feature Shuffle Weighted Group (FSWG) by applying mutual information shuffle and fusion. Moreover, to mitigate the negative effects of intermediate feature loss, we introduce Wide Residual Weighting units within WDIB. These units effectively fuse features of varying levels of detail through a Wide-residual Distillation Connection (WRDC) and a Self-Calibrating Fusion (SCF). To compensate for global feature deficiencies, we incorporate a Transformer and explore a novel architecture to combine CNN and Transformer. We show that our FIWHN achieves a favorable balance between performance and efficiency through extensive experiments on low-level and high-level tasks. © 1999-2012 IEEE.

关键词： Transformers Convolutional neural networks Computational modeling Feature extraction Training Superresolution Adaptation models Computer architecture Telecommunications Image reconstruction

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Thermal-Torsion Coupling Loading Analysis and Optimization of 3D PoP Assembly Solder Joints at Different Temperatures

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IEEE Transactions on Components, Packaging and Manufacturing Technology 2025年

作者： Gao, Chao Huang, Chunyue Liang, Yin Dian, Chen Lan, Jingyi Wang, Gui Guilin University of Electronic Technology School of Electrical and Mechanical Engineering Guilin541004 China Chengdu University Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province Chengdu610106 China Chengdu Aeronautic Vocational and Technical College Chengdu610021 China

A simulation analysis was conducted to investigate the stress distribution of Package-on-Package (PoP) assembly array solder joints subjected to thermal-torsional coupling loads, with the validity of the simulation confirmed through coupled loading stress-strain measurement experiments. The volume-weighted average method was employed to calculate the average equivalent stress (Δσ) of the critical solder joint danger element layer within the PoP assembly array, using Δσ as the evaluation benchmark. The impact and sensitivity of structural parameters on the thermal-torsional coupling stress of solder joints were examined. A structural parameter sensitivity model was developed to facilitate the stability and optimization analysis of the structural parameter intervals. The results indicate that solder joints in the lower array of the PoP assembly experience higher thermal-torsional coupling stress, with the peak internal stress occurring at the interface between the solder joint and the copper pad. The variation in thermal-torsional coupling stress of the array solder joints is more pronounced in the low-temperature region compared to the high-temperature region, with maximum stress occurring at -55°C and minimum stress observed at 125°C. Within the structural parameter range, the Δσ value of the critical solder joint shows a negative correlation with solder joint height, solder joint diameter, and substrate thickness, and a positive correlation with PCB thickness. The sensitivity to structural parameters follows the order: solder joint diameter, solder joint height, PCB thickness, and substrate thickness. Finally, the optimal range of structural parameters was determined, and the stability of this range was assessed. © 2011-2012 IEEE.

关键词： Sensitivity analysis

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Task-Oriented Multi-user Semantic Communication based on Deep Over-the-Air Computation

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IEEE Transactions on Vehicular Technology 2025年

作者： Wei, Shuangying Feng, Chunyan Guo, Caili Zhang, Biling Chen, Jiujiu Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Nanchang Hangkong University Key Laboratory of Processing and Pattern Recognition of Jiangxi Province School of Information Engineering Nanchang330063 China

The presence of large-scale heterogeneous IoT devices and AI-based applications has brought significant transmission and computation pressure on the wireless communication system. In this paper, we propose a novel multi-user semantic communication framework based on deep over-the-air computation called SC-DAC to tackle this challenge. By integrating communication and computation through a deep over-the-air computation (AirComp) encoder, SC-DAC is able to utilize the superposition of multiple access channels, facilitating multi-user information fusion during transmission and eliminating the need for multi-user detection at the receiver. Further, to achieve the optimal task results, the semantic encoders, decoders, and AirComp encoder are jointly optimized, where stochastic gradient descent (SGD) algorithm and deep double-Q network (DDQN) are employed to optimize the encoder/decoder parameters. The simulation results demonstrate that as the number of users increased, the performance of the system remains relatively stable and shows potential in resource utilization. What's more, when applied to the AI-based tasks such as multi-user multimodal action recognition and semantic segmentation, SC-DAC enhances the accuracy of action recognition by 26% compared with traditional methods and by about 30%-50% compared with other multi-user semantic communication methods under conditions of low signal-to-noise ratio (SNR), while it enhances the accuracy of semantic segmentation by 0.22 to 0.32 compared to other methods © 1967-2012 IEEE.

关键词： Deep reinforcement learning

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Metasurface-Loaded Circularly Polarized Dual-Band MIMO Cellphone Frame Antenna for Mobile Communication Applications

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IEEE Antennas and Wireless Propagation Letters 2025年

作者： Meng, Yanfeng Ren, Aidi Wang, Chao Xu, Guanghui Zhao, Luyu Li, Yingsong Yang, Lixia Huang, Zhi-Xiang Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Hefei230039 China Anhui University School of Electronic and Information Engineering Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230039 China Anhui Province Key Laboratory of Target Recognition and Feature Extraction Anhui237010 China Anhui University Key Laboratory of Electromagnetic Environment al Sensing of Anhui Higher Education Institutes Hefei230039 China

In this letter, a set of circularly polarized (CP) multiple-input multiple-output (MIMO) cellphone frame antennas for mobile communication applications is proposed. The planar inverted-F antenna (PIFA) and monopole antenna operate at frequencies of 3.5 GHz and 5.5 GHz with impedance and axial ratio (AR) bandwidths overlapping at 11.4% (3.3-3.7 GHz) and 7.3% (5.2-5.6 GHz), respectively. To reduce the electromagnetic coupling between the two antennas, the V-shaped mental fence is added and different rectangular metasurface (RMS) arrays are adopted to enhance the CP radiation performances. This design not only covers 5G communication, LTE, and the 5 GHz Wi-Fi band. It also provides a communication strategy for future direct satellite communication from cellphones, enhancing efficient switching between satellite and cellular networks. © 2002-2011 IEEE.

关键词： Monopole antennas

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TNIE-SLAM: Neural Implicit Surface Reconstruction for Tracking-Oriented SLAM

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Journal of intelligent & Robotic Systems 2025年第2期111卷 1-22页

作者： Gan, Baolin Zhang, Congxuan Chen, Shuaixin Chen, Zhen He, Chao Lu, Ke Lu, Feng School of Instrument Science and Optoelectronic Engineering Nanchang Hangkong University Nanchang China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China School of Information Engineering Nanchang Hangkong University Nanchang China The College of Engineering Science University of Chinese Academy of Sciences Beijing China

Recent studies on simultaneous localization and mapping (SLAM) have tended to employ implicit neural representation, which can improve the efficiency and robustness of SLAM system. However, these methodologies still face challenges, such as tracking failures and low-precision mapping. In this paper, we propose a dense reconstruction visual SLAM system enhanced with closed-loop threading and local map optimization, named TNIE-SLAM. First, we propose a tracking module that utilizes the similarity of ORB feature descriptors and the feature overlap rate of the current frame to model key frames, and then we define a complete and accurate initial map based on full bundle adjustment, which addresses the issue of tracking failure due to undermapped areas. Second, we add the 2D features of the initial map to the spatiotemporal encoding module to obtain the 3D features, enabling real-time prediction and tracking of unknown areas. Finally, considering the low-precision mapping issue arising from the complex geometric shapes of objects within the scene, we propose a local map optimization module that utilizes truncated signed distance fields to model 3D features and update the spatial occupancy of boundary and contour features of objects. We test our method on the synthetic Replica dataset and the real-world ScanNet and TUM RGB-D datasets to compare with some state-of-the-art RGB-D SLAM methods, and the experimental results indicate our method performs well in both tracking and mapping accuracy, surpassing the existing dense neural RGB-D SLAM methods.

关键词： Surface reconstruction

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Representation Auto-fused NMF based Hierarchical Clustering

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Expert Systems with Applications 2025年 283卷

作者： Lin, Yunxia Hu, Haoyu Li, Bentian Zhao, Shuchang Jing, Hengchang School of Information Engineering Yangzhou University Yangzhou225127 China Jiangsu Province Engineering Research Center of Knowledge Management and Intelligent Service Yangzhou University Yangzhou225127 China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Institute of Intelligent Information Processing Taizhou University Taizhou318000 China Taizhou University Taizhou318000 China

Nonnegative matrix factorization (NMF) as a powerful representation learning technique has been extensively applied in clustering tasks across diverse domains, leading to the proliferation of various NMF-based clustering methods. However, most of these methods suffer from the suboptimal performance arising from the disjointed process of representation learning and clustering, and the necessity to manually predetermine the optimal number of clusters which is a critical factor affecting the quality of clustering results. To fill this gap and inspired by convex clustering, we propose a simple yet effective NMF-based clustering method named Representation Auto-fused NMF based Hierarchical Clustering (RAFNMF-HC) by incorporating the l2 norm penalty on the differences between pairs of representations into the NMF framework to foster the automatic fusion of representations during representation learning. As the fusion continuously proceeds, similar representations gradually coalesce into small clusters, which subsequently merge into larger ones until a largest cluster encompassing all representations is formed, enabling a joint representation learning and clustering paradigm. Moreover, a cluster hierarchy as a byproduct adaptively emerges without requiring a predefined number of clusters, thus providing us with interpretability of cluster structure of data to some degree. Additionally, RAFNMF-HC only incorporates a single hyperparameter, significantly simplifying the adjustment process and boosting its practical efficiency. Our proposed RAFNMF-HC method also boasts straightforward scalability to diverse NMF variants, enhancing its versatility. Extensive experiments on various real-world datasets demonstrate the superiority of RAFNMF-HC. © 2025 Elsevier Ltd

关键词： Hierarchical clustering

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Deep Learning in Palmprint recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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