IF 6.7 4区计算机科学

Visual Computing for Industry Biomedicine and Art Pub Date : 2026-10-19 DOI: 10.1186/s42492-026-00235-z

Zhiping Dan, Run Zhang, Tinglong Tang, Yirong Wu, Xiaosheng Yu, Shuifa Sun, Ben Wang

{"title":"Swin-WaveNet: frequency-guided boundary restoration for robust skin lesion segmentation.","authors":"Zhiping Dan, Run Zhang, Tinglong Tang, Yirong Wu, Xiaosheng Yu, Shuifa Sun, Ben Wang","doi":"10.1186/s42492-026-00235-z","DOIUrl":"https://doi.org/10.1186/s42492-026-00235-z","url":null,"abstract":"Accurate skin lesion segmentation remains difficult when dermoscopic boundaries are fragmented by hair occlusion, uneven illumination, or weak local contrast. To address this problem, the authors propose Swin-WaveNet, a frequency-guided encoder-decoder that combines a Swin Transformer backbone with wavelet decomposition and selective state-space modeling. Rather than passing skip features directly to the decoder, the proposed method separates low-frequency semantic information from high-frequency details, selectively refines boundary-related responses, and suppresses artifact-induced activation prior to feature reconstruction. The main results are reported as the mean and standard deviation of the sample over three runs. Swin-WaveNet achieved a Dice score of 89.69% ± 0.51% and a 95th-percentile Hausdorff distance (HD95) of [Formula: see text] pixels on the International Skin Imaging Collaboration (ISIC) 2018 dataset. Without fine-tuning on ISIC 2017, it obtained a Dice score of 91.69% ± 1.36% and an HD95 of [Formula: see text] pixels. On Kvasir-SEG, the model achieved a Dice score of 91.63% ± 0.57% and an HD95 of [Formula: see text] pixels. In a single-run component ablation, the complete model improved the Dice score by 0.96 percentage points and reduced HD95 by 1.16 pixels relative to the internal baseline. These results indicate that frequency-selective feature processing can help preserve weak or interrupted lesion boundaries.","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149224875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DA-S²RL: Disease-aware shared-specific representation learning for heterogeneous image-EHR Alzheimer's disease diagnosis. DA-S2RL：异构图像- ehr诊断阿尔茨海默病的疾病感知共享特异性表征学习。

IF 7.2 1区计算机科学

Neural Networks Pub Date : 2026-10-19 DOI: 10.1016/j.neunet.2026.109760

Wei Wang, Yu Shi, Juan Cheng, Yu Liu

{"title":"DA-S2RL: Disease-aware shared-specific representation learning for heterogeneous image-EHR Alzheimer's disease diagnosis.","authors":"Wei Wang, Yu Shi, Juan Cheng, Yu Liu","doi":"10.1016/j.neunet.2026.109760","DOIUrl":"https://doi.org/10.1016/j.neunet.2026.109760","url":null,"abstract":"Combining electronic health records (EHR) with medical images is important for improving the diagnosis of Alzheimer's disease (AD). However, neuroimages and EHR differ greatly in semantic content and data distribution. Neuroimages mainly reflect brain structure and metabolic abnormalities, while EHR records clinical symptoms and behavioral changes. Directly fusing these heterogeneous modalities may mix shared disease information with modality-specific diagnostic cues, resulting in redundant and ambiguous representations. To address this problem, we propose DA-S2RL, a disease-aware shared-specific multimodal representation learning framework for AD diagnosis. DA-S2RL organizes heterogeneous image and EHR features into disease-related shared semantics and modality-specific diagnostic residuals. The core of DA-S2RL is the shared-specific cross-modal coding (SCC) module. It strengthens cross-modal semantic consistency, suppresses redundant correlations, and enables mutual refinement between shared semantics and diagnostic residuals. Before SCC, image features are integrated into a compact image representation, and EHR features are encoded into the same feature space. Experiments on ADNI and NACC demonstrate that explicitly organizing heterogeneous image-EHR representations into shared disease semantics and modality-specific residuals improves multimodal diagnostic learning under different image-EHR settings.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"206 Pt A","pages":"109760"},"PeriodicalIF":7.2,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating Explainable AI with Humans: Trust, Reliance, Understanding, and Performance 用人类评估可解释的人工智能：信任、依赖、理解和表现

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-10-19 DOI: 10.1145/3856819

Jonathan Frez Zachary, Nelson Baloian, Luciano Ahumada, Ashot Harutyunyan

{"title":"Evaluating Explainable AI with Humans: Trust, Reliance, Understanding, and Performance","authors":"Jonathan Frez Zachary, Nelson Baloian, Luciano Ahumada, Ashot Harutyunyan","doi":"10.1145/3856819","DOIUrl":"https://doi.org/10.1145/3856819","url":null,"abstract":"Explainable AI (XAI) is often evaluated through claims about trust, reliance, understanding, and performance, yet studies measure these constructs differently and frequently overlook confounding factors. This PRISMA-guided systematic review with narrative synthesis examines human-centered XAI evaluation across these four constructs. The review covers publications from 2015 to 2025. A merged search combined an OpenAlex-informed DOI/title catalog, prior surveys, citation chasing, and a Scopus coverage-completion stage identifying 9,026 records. A targeted revision update added two eligible publications, yielding 119 validated full-text publications. The synthesis highlights three patterns: trust and reliance can diverge; perceived understanding can exceed objective understanding; and interface salience or perceived accuracy can confound explanation effects. Aggregate advice acceptance, for example, cannot distinguish beneficial reliance from automation bias. We therefore recommend measuring acceptance separately for correct and incorrect AI outputs, testing understanding beyond self-report, documenting study context and interface conditions, and evaluating explanations under uncertainty or model failure. The review contributes construct definitions, a synthesis across designs and domains, an analysis of evaluation measures, and practical reporting guidelines. It also examines generated explanations and citation-based interfaces, supporting more reproducible and interpretable human-centered XAI evaluations.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"5 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149218727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection and Mitigation of Data Integrity Attacks in Vehicle Platoons Using Unknown-Input Kalman Filters and Penalized Weighted Least Squares. 基于未知输入卡尔曼滤波和惩罚加权最小二乘的车辆队列数据完整性攻击检测与缓解。

IF 11.3 1区计算机科学

IEEE Transactions on Cybernetics Pub Date : 2026-10-19 DOI: 10.1109/TCYB.2026.3740509

Chirag Tiwari Rao, Amir Dadashi, Amir Ameli, Hassan Naser

{"title":"Detection and Mitigation of Data Integrity Attacks in Vehicle Platoons Using Unknown-Input Kalman Filters and Penalized Weighted Least Squares.","authors":"Chirag Tiwari Rao, Amir Dadashi, Amir Ameli, Hassan Naser","doi":"10.1109/TCYB.2026.3740509","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3740509","url":null,"abstract":"This study enhances the security of vehicle platoon systems against false data injection attacks (FDIAs), which can compromise critical states, such as position, velocity, and acceleration. The proposed approach ensures safe and stable autonomous vehicle coordination by detecting and mitigating such attacks under flexible platoon conditions. Unlike the existing methods, it supports arbitrary communication topologies, varying platoon sizes, dynamic lead-vehicle behavior, and accounts for process and measurement noise-making it applicable to a wide range of real-world scenarios. A state-space model is formulated to capture the system dynamics, and an unknown input Kalman filter (UIKF) is employed to estimate both vehicle states and the lead vehicles acceleration as an unknown input. To counteract malicious data, a penalized weighted least squares (PWLSs) method is used to downweight suspicious measurements based on discrepancies between the observed and estimated values. The simulation results demonstrate the effectiveness of the proposed framework in detecting and mitigating FDIAs.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":11.3,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Adaptive Background-Attentive Network With Anomaly Suppression for Hyperspectral Anomaly Detection. 基于异常抑制的自适应背景关注网络高光谱异常检测。

IF 9.7 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2026-10-19 DOI: 10.1109/TNNLS.2026.3741742

Xu Liu, Hao Yuan, Bohui Tang, Qingquan Li, Chao Yang, Tiezhu Shi, Daming Zhu

{"title":"Self-Adaptive Background-Attentive Network With Anomaly Suppression for Hyperspectral Anomaly Detection.","authors":"Xu Liu, Hao Yuan, Bohui Tang, Qingquan Li, Chao Yang, Tiezhu Shi, Daming Zhu","doi":"10.1109/TNNLS.2026.3741742","DOIUrl":"https://doi.org/10.1109/TNNLS.2026.3741742","url":null,"abstract":"In recent years, deep learning has emerged as one of the prominent technologies in hyperspectral anomaly detection (HAD) due to its powerful feature extraction capability. Deep learning-based methods aim to reconstruct the background image and use the reconstruction error as a criterion for anomaly detection. However, existing methods rarely exploit the spatial and spectral differences between background and anomalies to suppress anomaly reconstruction. To overcome this limitation, we propose a self-adaptive background-attentive network with anomaly suppression (BASNet). Specifically, a pyramid similarity downsampling module (PSDM) is employed to reduce the proportion of anomalies in the input image and improve multiscale background representation. To enhance the network's attention to background information, a multiscale attention module (MAM) is designed, and an inverse attention strategy is adopted to guide the network to focus on background regions. In addition, a depthwise separable blind-block convolution module (DBCM) is constructed for pixel reconstruction. This module applies weighted multiscale blind-block convolutions according to the anomaly score of each pixel, allowing the network to effectively handle anomalies at different scales. Due to the stochastic nature of deep learning, the optimal stopping epoch for network training is not fixed. Therefore, reconstructed image difference (RID) is developed to dynamically determine the stopping epoch. Extensive experiments on eight datasets demonstrate that the proposed BASNet achieves satisfactory detection performance.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AGformer: Adaptive filtering and gated fusion-attention for multivariate time series forecasting. 多变量时间序列预测的自适应滤波和门控融合注意。

IF 7.2 1区计算机科学

Neural Networks Pub Date : 2026-10-19 DOI: 10.1016/j.neunet.2026.109759

Shu-Xin Xu, Jiang-Wen Xiao, Peng-Cheng Li, Tian-Hao Lu

{"title":"AGformer: Adaptive filtering and gated fusion-attention for multivariate time series forecasting.","authors":"Shu-Xin Xu, Jiang-Wen Xiao, Peng-Cheng Li, Tian-Hao Lu","doi":"10.1016/j.neunet.2026.109759","DOIUrl":"https://doi.org/10.1016/j.neunet.2026.109759","url":null,"abstract":"Multivariate time series forecasting is a fundamental problem in sequence modeling, with broad applications spanning energy systems, meteorology, finance, and transportation. Despite significant progress in Transformer-based, TCN-based, and MLP-based architectures, three persistent neural network design challenges remain unresolved: 1) non-adaptive frequency-domain filtering that applies identical spectral parameters across heterogeneous variables; 2) inadequate utilization of informative short-term fluctuations that are discarded as noise yet encode critical event-driven dynamics; and 3) insufficient exploration of inter-scale dependencies in multi-scale temporal representations. To address these challenges, this paper proposes AGformer, a novel forecasting architecture integrating three complementary neural modules: 1) A variable-oriented Adaptive Multi-k Filter (AKF) that dynamically generates per-variable spectral filtering parameters from learned frequency-domain features, eliminating the need for manual global hyperparameter tuning. 2) A Dual-Gate Fusion-Attention (DGFA) mechanism that synergistically models long-term trends and short-term fluctuations via complementary temporal and content gating, recovering event-driven signals that are otherwise discarded by standard denoising. 3) A Multi-Scale Embedding (MSED) layer that fuses temporal patterns across resolutions through attention-based inter-scale weighting, capturing cross-resolution hierarchical dependencies that simple concatenation or summation cannot model. Extensive experiments on nine representative benchmark datasets across electricity, weather, finance, and transportation domains demonstrate AGformer's consistent superiority, achieving average reductions of 8.1%-23.6% in MSE and MAE relative to state-of-the-art baselines across nine benchmarks.","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"206 Pt A","pages":"109759"},"PeriodicalIF":7.2,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reusability report: multi-sample omics network inference operates in distinct robustness regimes defined by prior knowledge and data availability 可重用性报告：多样本组学网络推理在由先验知识和数据可用性定义的不同鲁棒性制度中运行

IF 29.8 1区计算机科学

Nature Machine Intelligence Pub Date : 2026-10-19 DOI: 10.1038/s42256-026-01311-y

Xabier Martínez-de-Morentin, Sumeer A. Khan, Itziar Cenzano, Robert Lehmann, Alberto Maillo, Vincenzo Lagani, Jesper Tegner, David Gomez-Cabrero

{"title":"Reusability report: multi-sample omics network inference operates in distinct robustness regimes defined by prior knowledge and data availability","authors":"Xabier Martínez-de-Morentin, Sumeer A. Khan, Itziar Cenzano, Robert Lehmann, Alberto Maillo, Vincenzo Lagani, Jesper Tegner, David Gomez-Cabrero","doi":"10.1038/s42256-026-01311-y","DOIUrl":"10.1038/s42256-026-01311-y","url":null,"abstract":"Knowledge-constrained network inference enables the reconstruction of regulatory programmes from high-dimensional biological data, yet the conditions under which shared and context-specific structures can be reliably identified remain poorly understood. Pablo Rodriguez-Mier et al. recently introduced CORNETO, a framework for multi-sample constrained inference. Here we reproduce, evaluate and extend CORNETO in terms of robustness, identifiability and bias–variance trade-offs. Using dynamic haematopoiesis and stable cortical datasets, we systematically assess four network reconstruction paradigms while preserving the original method-specific evaluation procedures and benchmark definitions. We show that multi-sample inference generally improves robustness and reproducibility, although gains in predictive performance depend on biological context and the completeness of the prior network. To enhance stability, we introduce bootstrap aggregation and selective re-expansion strategies that improve consensus robustness and recovery while preserving context-specific signals without modifying the underlying optimization scheme or sacrificing generality. Across datasets and methods, our results reveal distinct inference regimes governed by sample availability, prior knowledge and regularization. Together, these findings position network reconstruction as a regime-dependent learning problem and provide practical principles for robust multi-sample biological network inference. This work studies the reusability of CORNETO, a previously developed framework for learning biological networks from multi-omics data. Robust network inference is found to depend on data availability, prior knowledge, regularization and biological context.","PeriodicalId":48533,"journal":{"name":"Nature Machine Intelligence","volume":"8 10","pages":"1616-1631"},"PeriodicalIF":29.8,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.comhttps://www.nature.com/articles/s42256-026-01311-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149219603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond continuance: how FinTech app experiences shape user well-being 超越延续：金融科技应用体验如何塑造用户幸福感

IF 5.9 3区管理学

Internet Research Pub Date : 2026-10-19 DOI: 10.1108/intr-04-2026-0726

Ali Raza,Shehab Abdulhabib Saeed Alzaeemi,Mousa Albashrawi,Yogesh K. Dwivedi,Inyoung Chae

{"title":"Beyond continuance: how FinTech app experiences shape user well-being","authors":"Ali Raza,Shehab Abdulhabib Saeed Alzaeemi,Mousa Albashrawi,Yogesh K. Dwivedi,Inyoung Chae","doi":"10.1108/intr-04-2026-0726","DOIUrl":"https://doi.org/10.1108/intr-04-2026-0726","url":null,"abstract":"Purpose This study examines how users' everyday experiences with digital wallet apps, as a major category of FinTech applications, shape their well-being rather than focusing only on continued use. Using real user reviews from Saudi Arabia, it identifies four key experiential factors: complaint handling effectiveness, perceived system reliability, information ambiguity, and user autonomy. Drawing on Self-Determination Theory (SDT), the study explains how these factors shape user well-being. Design/methodology/approach We applied a sequential mixed-methods design guided by Computational User Feedback Analytics (CUFA) to Arabic and English reviews collected from the Google Play Store and Apple App Store. CUFA integrates hierarchical clustering, Latent Dirichlet Allocation( topic modeling, sentiment analysis, multi-model classification, human validation, and regularized regression. We interpreted the review-derived constructs through SDT and tested them using Ridge and Lasso regression. Findings Our findings indicate that complaint handling effectiveness, perceived system reliability, and user autonomy positively shape user well-being. In contrast, information ambiguity negatively affects user well-being. Originality/value Unlike prior studies that rely predominantly on structured surveys and predefined adoption models, this study applies CUFA to derive, validate, and test theoretically meaningful constructs using large-scale, unsolicited user feedback. It also extends SDT into the fintech context by showing how specific digital wallet experiences support or frustrate users' psychological needs.","PeriodicalId":54925,"journal":{"name":"Internet Research","volume":"33 1","pages":"1-20"},"PeriodicalIF":5.9,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intelligent Cyberattack Detection in the Internet of Vehicles (IoV) Using Hybrid Deep Learning Techniques 基于混合深度学习技术的车联网智能网络攻击检测

IF 2.6 4区计算机科学

Transactions on Emerging Telecommunications Technologies Pub Date : 2026-10-19 DOI: 10.1002/ett.70501

Pabitra dubey, Deepika Agrawal

{"title":"Intelligent Cyberattack Detection in the Internet of Vehicles (IoV) Using Hybrid Deep Learning Techniques","authors":"Pabitra dubey, Deepika Agrawal","doi":"10.1002/ett.70501","DOIUrl":"https://doi.org/10.1002/ett.70501","url":null,"abstract":"<div>\u0000 \u0000 Rapid growth in the IoV domain has led to significant security risks, making it imperative to use state-of-the-art intrusion detection systems. Conventional methods usually lack the ability to effectively learn spatial, temporal, and global properties from the vehicular traffic data streams. Therefore, this study introduces a novel IDS named FusionIDS. It is different from previous convolutional neural network long short-term memory (CNN-LSTM) approaches in that this study considers the integration of Transformer-based global attention along with SHAP explanations that are specific to IoV IDS. FusionIDS consists of three components, namely, a 1-dimensional convolutional neural network (1D-CNN) for extracting local features, an LSTM network for learning sequential information, and a Transformer encoder for global information extraction. In addition, it uses SMOTE to manage the problem of severe class imbalance and SHapley Additive exPlanations (SHAP) for explainability. Results show that FusionIDS obtains around 98% accuracy when tested on various attack cases in IoV environments.\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"37 11","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149214718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking Large Language Models for Video Quality Assessment: Structured Perceptual Prompting and Refinement 面向视频质量评估的大型语言模型的基准测试：结构化感知提示和改进

IF 19.5 2区计算机科学

International Journal of Computer Vision Pub Date : 2026-10-18 DOI: 10.1007/s11263-026-03068-8

Runze Hu,Mingxuan Li,Yutao Liu,Haoran Duan,Yan Zhang

引用次数: 0

计算机科学最新文献