Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

Lost data reconstruction for structural health monitoring using deep convolutional generative adversarial networks

Lei, X., Sun, L., & Xia, Y. (2021) · Structural Health Monitoring · doi:10.1177/1475921720959226 · Cited by 236

Fine-Tuning and training of densenet for histopathology image representation using TCGA diagnostic slides

Riasatian, A., Babaie, M., Maleki, D., Kalra, S., Valipour, M., Hemati, S., Zaveri, M., Safarpoor, A., Shafiei, S., Afshari, M., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102032 · Cited by 221

Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies

Kenny, E. M., Ford, C., Quinn, M., & Keane, M. T. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103459 · Cited by 211

Generative AI design for building structures

Liao, W., Lu, X., Fei, Y., Gu, Y., & Huang, Y. (2024) · Automation in Construction · doi:10.1016/j.autcon.2023.105187 · Cited by 213

Machine vision for natural gas methane emissions detection using an infrared camera

Wang, J., Tchapmi, L. P., Ravikumar, A. P., McGuire, M., Bell, C. S., Zimmerle, D., Savarese, S., & Brandt, A. R. (2020) · Applied Energy · doi:10.1016/j.apenergy.2019.113998 · Cited by 205

Country-wide high-resolution vegetation height mapping with Sentinel-2

Lang, N., Schindler, K., & Wegner, J. D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111347 · Cited by 202

Dual-force convolutional neural networks for accurate brain tumor segmentation

Chen, S., Ding, C., & Liu, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.009 · Cited by 195

Hybrid robust convolutional autoencoder for unsupervised anomaly detection of machine tools under noises

Yan, S., Shao, H., Xiao, Y., Liu, B., & Wan, J. (2023) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102441 · Cited by 201

Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions

Ahmad, S., Shakeel, I., Mehfuz, S., & Ahmad, J. (2023) · Computer Science Review · doi:10.1016/j.cosrev.2023.100568 · Cited by 197

Readmission prediction using deep learning on electronic health records

Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103256 · Cited by 194

A survey of techniques for optimizing deep learning on gpus

Mittal, S., & Vaishay, S. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2019.101635 · Cited by 197

Cross Modal Transformer: Towards Fast and Robust 3D Object Detection

Yan, J., Liu, Y., Sun, J., Jia, F., Li, S., Wang, T., & Zhang, X. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01675 · Cited by 182

Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01834 · Cited by 182

Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines

Zhang, J., Li, Y., Xiao, W., & Zhang, Z. (2020) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2020.04.033 · Cited by 179

A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving

Elallid, B. B., Benamar, N., Hafid, A. S., Rachidi, T., & Mrani, N. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.03.013 · Cited by 179

Multimodal big data affective analytics: A comprehensive survey using text, audio, visual and physiological signals

Shoumy, N. J., Ang, L.-M., Seng, K. P., Rahaman, D. M. M., & Zia, T. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.102447 · Cited by 171

Skin disease diagnosis with deep learning: a review

Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178

BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights

Benhammou, Y., Achchab, B., Herrera, F., & Tabik, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.09.044 · Cited by 170

Generating 3D city models without elevation data

Biljecki, F., Ledoux, H., & Stoter, J. (2017) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2017.01.001 · Cited by 165

Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning

Bi, X., & Wang, H. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.02.005 · Cited by 169

Online accurate state of health estimation for battery systems on real-world electric vehicles with variable driving conditions considered

Hong, J., Wang, Z., Chen, W., Wang, L., Lin, P., & Qu, C. (2021) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.125814 · Cited by 166

OCRBench: on the hidden mystery of OCR in large multimodal models

Liu, Y., Li, Z., Huang, M., Yang, B., Yu, W., Li, C., Yin, X.-C., Liu, C.-L., Jin, L., & Bai, X. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4235-6 · Cited by 167

Human-like problem-solving abilities in large language models using ChatGPT

Orrù, G., Piarulli, A., Conversano, C., & Gemignani, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1199350 · Cited by 170

Learning supervised scoring ensemble for emotion recognition in the wild

Hu, P., Cai, D., Wang, S., Yao, A., & Chen, Y. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3143009 · Cited by 162

Computer vision based target-free 3D vibration displacement measurement of structures

Shao, Y., Li, L., Li, J., An, S., & Hao, H. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.113040 · Cited by 161

Deep Learning on Mobile and Embedded Devices: State-of-the-art, Challenges, and Future Directions

Chen, Y., Zheng, B., Zhang, Z., Wang, Q., Shen, C., & Zhang, Q. (2021) · ACM Computing Surveys · doi:10.1145/3398209 · Cited by 159

A deep learning framework for road marking extraction, classification and completion from mobile laser scanning point clouds

Wen, C., Sun, X., Li, J., Wang, C., Guo, Y., & Habib, A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.10.007 · Cited by 158

Ultra-Degree-of-Freedom Structured Light for Ultracapacity Information Carriers

Wan, Z., Wang, H., Liu, Q., Fu, X., & Shen, Y. (2023) · ACS Photonics · doi:10.1021/acsphotonics.2c01640 · Cited by 156

EAMM: One-Shot Emotional Talking Face via Audio-Based Emotion-Aware Motion Model

Ji, X., Zhou, H., Wang, K., Wu, Q., Wu, W., Xu, F., & Cao, X. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings · doi:10.1145/3528233.3530745 · Cited by 155

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Srivastava, S., Vargas-Muñoz, J. E., & Tuia, D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.04.014 · Cited by 151

LLDiffusion: Learning degradation representations in diffusion models for low-light image enhancement

Wang, T., Zhang, K., Zhang, Y., Luo, W., Stenger, B., Lu, T., Kim, T.-K., & Liu, W. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111628 · Cited by 150

Digital image correlation (DIC) based damage detection for CFRP laminates by using machine learning based image semantic segmentation

Wang, Y., Luo, Q., Xie, H., Li, Q., & Sun, G. (2022) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2022.107529 · Cited by 149

NuClick: A deep learning framework for interactive segmentation of microscopic images

Alemi Koohbanani, N., Jahanifar, M., Zamani Tajadin, N., & Rajpoot, N. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101771 · Cited by 147

Cross-subject transfer learning in human activity recognition systems using generative adversarial networks

Soleimani, E., & Nazerfard, E. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.10.056 · Cited by 146

Efficient densely connected convolutional neural networks

Li, G., Zhang, M., Li, J., Lv, F., & Tong, G. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107610 · Cited by 145

Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference

Li, B., Dai, Y., & He, M. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.05.029 · Cited by 142

Interpretation of deep learning in genomics and epigenomics

Talukder, A., Barham, C., Li, X., & Hu, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa177 · Cited by 142

Defect detection of hot rolled steels with a new object detection framework called classification priority network

He, D., Xu, K., & Zhou, P. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.12.043 · Cited by 140

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

He, S., Leanse, L. G., & Feng, Y. (2021) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2021.113922 · Cited by 141

A review of multimodal emotion recognition from datasets, preprocessing, features, and fusion methods

Pan, B., Hirota, K., Jia, Z., & Dai, Y. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126866 · Cited by 142

Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images

Kalra, S., Tizhoosh, H. R., Choi, C., Shah, S., Diamandis, P., Campbell, C. J. V., & Pantanowitz, L. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101757 · Cited by 139

"Person, Shoes, Tree. Is the Person Naked?" What People with Vision Impairments Want in Image Descriptions

Stangl, A., Morris, M. R., & Gurari, D. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376404 · Cited by 139

Bayesian QuickNAT: model uncertainty in deep whole-brain segmentation for structure-wise quality control

Roy, A. G., Conjeti, S., Navab, N., & Wachinger, C. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.03.042 · Cited by 137

Deep Learning for Mobile Multimedia: A Survey

Ota, K., Dao, M. S., Mezaris, V., & Natale, F. G. B. D. (2017) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3092831 · Cited by 135

Futures of artificial intelligence through technology readiness levels

Martínez-Plumed, F., Gómez, E., & Hernández-Orallo, J. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2020.101525 · Cited by 130

FusionM4Net: a multi-stage multi-modal learning algorithm for multi-label skin lesion classification

Tang, P., Yan, X., Nan, Y., Xiang, S., Krammer, S., & Lasser, T. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102307 · Cited by 131

PAIP 2019: Liver cancer segmentation challenge

Kim, Y. J., Jang, H., Lee, K., Park, S., Min, S.-G., Hong, C., Park, J. H., Lee, K., Kim, J., Hong, W., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101854 · Cited by 131

Informative knowledge distillation for image anomaly segmentation

Cao, Y., Wan, Q., Shen, W., & Gao, L. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108846 · Cited by 130

MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning

Luo, X., Wang, G., Song, T., Zhang, J., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., & Zhang, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102102 · Cited by 126

BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions

Calisto, F. M., Santiago, C., Nunes, N., & Nascimento, J. C. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102285 · Cited by 125

ULIP-2: Towards Scalable Multimodal Pre-Training for 3D Understanding

Xue, L., Yu, N., Zhang, S., Panagopoulou, A., Li, J., Martín-Martín, R., Wu, J., Xiong, C., Xu, R., Niebles, J. C., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02558 · Cited by 144

Indicators and criteria of consciousness in animals and intelligent machines: an inside-out approach

Pennartz, C. M. A., Farisco, M., & Evers, K. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2019.00025 · Cited by 121

Long short-term memory-based deep recurrent neural networks for target tracking

Gao, C., Yan, J., Zhou, S., Varshney, P. K., & Liu, H. (2019) · Information Sciences · doi:10.1016/j.ins.2019.06.039 · Cited by 118

Autonomous deep learning: A genetic DCNN designer for image classification

Ma, B., Li, X., Xia, Y., & Zhang, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.007 · Cited by 120

Inception and ResNet features are (almost) equivalent

McNeely-White, D., Beveridge, J. R., & Draper, B. A. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2019.10.004 · Cited by 116

A review of deep learning‐based approaches for deepfake content detection

(2024) · doi:10.1111/exsy.13570 · Cited by 116

Deep learning and the global workspace theory

VanRullen, R., & Kanai, R. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.04.005 · Cited by 128

Memory-aware curriculum federated learning for breast cancer classification

Jiménez-Sánchez, A., Tardy, M., González Ballester, M. A., Mateus, D., & Piella, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107318 · Cited by 114

An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes

Han, Y., Tang, B., & Deng, L. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.01.012 · Cited by 111

Deep convolutional pooling transformer for deepfake detection

Wang, T., Cheng, H., Chow, K. P., & Nie, L. (2023) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3588574 · Cited by 111

A Generative Neural Network for Maximizing Fitness and Diversity of Synthetic DNA and Protein Sequences

Linder, J., Bogard, N., Rosenberg, A. B., & Seelig, G. (2020) · Cell Systems · doi:10.1016/j.cels.2020.05.007 · Cited by 107

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution

Chen, T.-H. K., Qiu, C., Schmitt, M., Zhu, X. X., Sabel, C. E., & Prishchepov, A. V. (2020) · Remote Sensing of Environment · doi:10.1016/j.rse.2020.112096 · Cited by 104

Cascaded multi-modal mixing transformers for Alzheimer’s disease classification with incomplete data

Liu, L., Liu, S., Zhang, L., To, X. V., Nasrallah, F., & Chandra, S. S. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120267 · Cited by 101

Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning

Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., & Noble, J. A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.006 · Cited by 95

Attention fusion network for multi-spectral semantic segmentation

Xu, J., Lu, K., & Wang, H. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.015 · Cited by 99

Query-by-committee improvement with diversity and density in batch active learning

Kee, S., del Castillo, E., & Runger, G. (2018) · Information Sciences · doi:10.1016/j.ins.2018.05.014 · Cited by 98

Testing human ability to detect ‘deepfake’ images of human faces

Bray, S. D., Johnson, S. D., & Kleinberg, B. (2023) · Journal of Cybersecurity · doi:10.1093/cybsec/tyad011 · Cited by 96

Passive acoustic monitoring of animal populations with transfer learning

Dufourq, E., Batist, C., Foquet, R., & Durbach, I. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101688 · Cited by 97

State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries

Yu, H., Zhang, L., Wang, W., Li, S., Chen, S., Yang, S., Li, J., & Liu, X. (2023) · Energy · doi:10.1016/j.energy.2023.127846 · Cited by 96

SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection

Ye, Y., Chen, H., Zhang, C., Hao, X., & Zhang, Z. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.09.086 · Cited by 95

Holodeck: Language Guided Generation of 3D Embodied AI Environments

Yang, Y., Sun, F.-Y., Weihs, L., Vanderbilt, E., Herrasti, A., Han, W., Wu, J., Haber, N., Krishna, R., Liu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01536 · Cited by 101

Automated Fetal Head Detection and Circumference Estimation from Free-Hand Ultrasound Sweeps Using Deep Learning in Resource-Limited Countries

van den Heuvel, T. L. A., Petros, H., Santini, S., de Korte, C. L., & van Ginneken, B. (2019) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2018.09.015 · Cited by 91

LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection

NGUYEN, D., MEJRI, N., SINGH, I. P., KULESHOVA, P., ASTRID, M., KACEM, A., GHORBEL, E., & AOUADA, D. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01647 · Cited by 93

A survey of multimodal learning: Methods, applications, and future

Yuan, Y., Li, Z., & Zhao, B. (2025) · ACM Computing Surveys · doi:10.1145/3713070 · Cited by 90

ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts

Cai, M., Liu, H., Mustikovela, S. K., Meyer, G. P., Chai, Y., Park, D., & Lee, Y. J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01227 · Cited by 88

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., Ha, S. M., Shukla, G., Flanders, A., Kotrotsou, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117081 · Cited by 84

Multimodal emotional state recognition using sequence-dependent deep hierarchical features

Barros, P., Jirak, D., Weber, C., & Wermter, S. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.09.009 · Cited by 86

Evolutionary double attention-based long short-term memory model for building energy prediction: Case study of a green building

Ding, Z., Chen, W., Hu, T., & Xu, X. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116660 · Cited by 85

RoboCup@Home: Analysis and results of evolving competitions for domestic and service robots

Iocchi, L., Holz, D., Ruiz-del-Solar, J., Sugiura, K., & van der Zant, T. (2015) · Artificial Intelligence · doi:10.1016/j.artint.2015.08.002 · Cited by 86

Designing efficient accelerator of depthwise separable convolutional neural network on FPGA

Ding, W., Huang, Z., Huang, Z., Tian, L., Wang, H., & Feng, S. (2019) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2018.12.008 · Cited by 85

Perception science in the age of deep neural networks.

VanRullen, R. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00142 · Cited by 82

Human activity recognition based on smartphone and wearable sensors using multiscale DCNN ensemble

Sena, J., Barreto, J., Caetano, C., Cramer, G., & Schwartz, W. R. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.04.151 · Cited by 81

Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning

Brehmer, J., Mishra-Sharma, S., Hermans, J., Louppe, G., & Cranmer, K. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab4c41 · Cited by 80

An empirical study of multi-scale object detection in high resolution UAV images

Zhang, H., Sun, M., Li, Q., Liu, L., Liu, M., & Ji, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.08.074 · Cited by 78

New Technologies for the Understanding, Assessment, and Intervention of Emotion Regulation

Colombo, D., Fernández-Álvarez, J., García Palacios, A., Cipresso, P., Botella, C., & Riva, G. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.01261 · Cited by 75

Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network

Pal, A., Garain, U., Chandra, A., Chatterjee, R., & Senapati, S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.027 · Cited by 74

Going Beyond One-Size-Fits-All Image Descriptions to Satisfy the Information Wants of People Who are Blind or Have Low Vision

Stangl, A., Verma, N., Fleischmann, K. R., Morris, M. R., & Gurari, D. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471233 · Cited by 81

Multi-view unsupervised feature selection with tensor robust principal component analysis and consensus graph learning

Liang, C., Wang, L., Liu, L., Zhang, H., & Guo, F. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109632 · Cited by 72

Small perturbations are enough: Adversarial attacks on time series prediction

Wu, T., Wang, X., Qiao, S., Xian, X., Liu, Y., & Zhang, L. (2022) · Information Sciences · doi:10.1016/j.ins.2021.11.007 · Cited by 72

Neighbor-view Enhanced Model for Vision and Language Navigation

An, D., Qi, Y., Huang, Y., Wu, Q., Wang, L., & Tan, T. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475282 · Cited by 74

Deep learning evaluation of biomarkers from echocardiogram videos

Hughes, J. W., Yuan, N., He, B., Ouyang, J., Ebinger, J., Botting, P., Lee, J., Theurer, J., Tooley, J. E., Nieman, K., et al. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103613 · Cited by 71

Super-resolution emulator of cosmological simulations using deep physical models

Kodi Ramanah, D., Charnock, T., Villaescusa-Navarro, F., & Wandelt, B. D. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1428 · Cited by 70

On-field player workload exposure and knee injury risk monitoring via deep learning

Johnson, W. R., Mian, A., Lloyd, D. G., & Alderson, J. A. (2019) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2019.07.002 · Cited by 69

NeuroSkinning: automatic skin binding for production characters with deep graph networks

Liu, L., Zheng, Y., Tang, D., Yuan, Y., Fan, C., & Zhou, K. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322969 · Cited by 67

3D-SSD: Learning hierarchical features from RGB-D images for amodal 3D object detection

Luo, Q., Ma, H., Tang, L., Wang, Y., & Xiong, R. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.10.025 · Cited by 69

AD-NET: Age-adjust neural network for improved MCI to AD conversion prediction

Gao, F., Yoon, H., Xu, Y., Goradia, D., Luo, J., Wu, T., & Su, Y. (2020) · NeuroImage: Clinical · doi:10.1016/j.nicl.2020.102290 · Cited by 64

Densely connected convolutional networks for vibration based structural damage identification

Wang, R., Li, J., Chencho, An, S., Hao, H., Liu, W., & Li, L. (2021) · Engineering Structures · doi:10.1016/j.engstruct.2021.112871 · Cited by 62

GeoAI in terrain analysis: Enabling multi-source deep learning and data fusion for natural feature detection

Wang, S., & Li, W. (2021) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101715 · Cited by 59

Understanding World or Predicting Future? A Comprehensive Survey of World Models

Ding, J., Zhang, Y., Shang, Y., Zhang, Y., Zong, Z., Feng, J., Yuan, Y., Su, H., Li, N., Sukiennik, N., et al. (2026) · ACM Computing Surveys · doi:10.1145/3746449 · Cited by 169

3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network

Hermoza, R., & Sipiran, I. (2018) · Proceedings of Computer Graphics International 2018 · doi:10.1145/3208159.3208173 · Cited by 56