2009 IEEE Conference on Computer Vision and Pattern Recognition

A systematic study of the class imbalance problem in convolutional neural networks

Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.07.011 · Cited by 3,088

A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update

Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239

Content-based multimedia information retrieval: State of the art and challenges

Lew, M. S., Sebe, N., Djeraba, C., & Jain, R. (2006) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/1126004.1126005 · Cited by 1,528

Apple detection during different growth stages in orchards using the improved YOLO-V3 model

Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.01.012 · Cited by 1,043

Data augmentation: A comprehensive survey of modern approaches

Mumuni, A., & Mumuni, F. (2022) · Array · doi:10.1016/j.array.2022.100258 · Cited by 891

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond

Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717

Sensor-based and vision-based human activity recognition: A comprehensive survey

Minh Dang, L., Min, K., Wang, H., Jalil Piran, M., Hee Lee, C., & Moon, H. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107561 · Cited by 730

Understanding adversarial attacks on deep learning based medical image analysis systems

Ma, X., Niu, Y., Gu, L., Wang, Y., Zhao, Y., Bailey, J., & Lu, F. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107332 · Cited by 553

A comprehensive review of object detection with deep learning

Kaur, R., & Singh, S. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2022.103812 · Cited by 539

Federated learning for medical image analysis: A survey

Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518

Deep Learning for Deepfakes Creation and Detection: A Survey

Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2019) · arXiv · doi:10.48550/arxiv.1909.11573 · Cited by 464

Deep learning for deepfakes creation and detection: A survey

Nguyen, T. T., Nguyen, Q. V. H., Nguyen, D. T., Nguyen, D. T., Huynh-The, T., Nahavandi, S., Nguyen, T. T., Pham, Q.-V., & Nguyen, C. M. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103525 · Cited by 464

Robust single-view geometry and motion reconstruction

Li, H., Adams, B., Guibas, L. J., & Pauly, M. (2009) · ACM Transactions on Graphics · doi:10.1145/1618452.1618521 · Cited by 334

Fusion of multispectral data through illumination-aware deep neural networks for pedestrian detection

Guan, D., Cao, Y., Yang, J., Cao, Y., & Yang, M. Y. (2019) · Information Fusion · doi:10.1016/j.inffus.2018.11.017 · Cited by 342

Computer vision for behaviour-based safety in construction: A review and future directions

Fang, W., Love, P. E. D., Luo, H., & Ding, L. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100980 · Cited by 346

A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4

Kalyan, K. S. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100048 · Cited by 335

Detecting mammals in UAV images: best practices to address a substantially imbalanced dataset with deep learning

Kellenberger, B., Marcos, D., & Tuia, D. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.06.028 · Cited by 335

A literature review of Artificial Intelligence applications in railway systems

Tang, R., De Donato, L., Bes̆inović, N., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Tang, T., Vittorini, V., & Wang, Z. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103679 · Cited by 324

Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges

Miglani, A., & Kumar, N. (2019) · Vehicular Communications · doi:10.1016/j.vehcom.2019.100184 · Cited by 295

A Review of the Deep Learning Methods for Medical Images Super Resolution Problems

Li, Y., Sixou, B., & Peyrin, F. (2021) · IRBM · doi:10.1016/j.irbm.2020.08.004 · Cited by 281

A Review of Deep Learning-Based Information Fusion Techniques for Multimodal Medical Image Classification

Li, Y., El Habib Daho, M., Conze, P.-H., Zeghlache, R., Le Boité, H., Tadayoni, R., Cochener, B., Lamard, M., & Quellec, G. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108635 · Cited by 274

Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning

Desislavov, R., Martínez-Plumed, F., & Hernández-Orallo, J. (2023) · Sustainable Computing: Informatics and Systems · doi:10.1016/j.suscom.2023.100857 · Cited by 269

C2TAM: A Cloud framework for cooperative tracking and mapping

Riazuelo, L., Civera, J., & Montiel, J. M. M. (2014) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2013.11.007 · Cited by 254

Deep metric learning for few-shot image classification: A Review of recent developments

Li, X., Yang, X., Ma, Z., & Xue, J.-H. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109381 · Cited by 243

Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation

Liu, K.-L., Wu, T., Chen, P.-T., Tsai, Y. M., Roth, H., Wu, M.-S., Liao, W.-C., & Wang, W. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30078-9 · Cited by 222

DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms

Qi, H., Guo, Q., Juefei-Xu, F., Xie, X., Ma, L., Feng, W., Liu, Y., & Zhao, J. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413707 · Cited by 220

A survey on few-shot class-incremental learning

Tian, S., Li, L., Li, W., Ran, H., Ning, X., & Tiwari, P. (2024) · Neural Networks · doi:10.1016/j.neunet.2023.10.039 · Cited by 220

Discriminative Models for Multi-Class Object Layout

Desai, C., Ramanan, D., & Fowlkes, C. C. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-011-0439-x · Cited by 208

The classification of construction waste material using a deep convolutional neural network

Davis, P., Aziz, F., Newaz, M. T., Sher, W., & Simon, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2020.103481 · Cited by 204

Guidelines and evaluation of clinical explainable AI in medical image analysis

Jin, W., Li, X., Fatehi, M., & Hamarneh, G. (2023) · Medical Image Analysis · doi:10.1016/j.media.2022.102684 · Cited by 199

Multi-modal deep learning for Fuji apple detection using RGB-D cameras and their radiometric capabilities

Gené-Mola, J., Vilaplana, V., Rosell-Polo, J. R., Morros, J.-R., Ruiz-Hidalgo, J., & Gregorio, E. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.05.016 · Cited by 169

Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning

Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2816 · Cited by 160

Artificial intelligence and machine learning for quantum technologies

Krenn, M., Landgraf, J., Foesel, T., & Marquardt, F. (2023) · Physical Review A · doi:10.1103/physreva.107.010101 · Cited by 162

Review of the robustness and applicability of monocular pose estimation systems for relative navigation with an uncooperative spacecraft

Pasqualetto Cassinis, L., Fonod, R., & Gill, E. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.05.008 · Cited by 156

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

Visual Imagery and Perception Share Neural Representations in the Alpha Frequency Band

Xie, S., Kaiser, D., & Cichy, R. M. (2020) · Current Biology · doi:10.1016/j.cub.2020.04.074 · Cited by 139

Multi-user activity recognition: Challenges and opportunities

Li, Q., Gravina, R., Li, Y., Alsamhi, S. H., Sun, F., & Fortino, G. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.004 · Cited by 140

Transformer for object detection: Review and benchmark

Li, Y., Miao, N., Ma, L., Shuang, F., & Huang, X. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107021 · Cited by 135

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

Enabling country-scale land cover mapping with meter-resolution satellite imagery

Tong, X.-Y., Xia, G.-S., & Zhu, X. X. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.011 · Cited by 125

Advances in intelligent and autonomous navigation systems for small UAS

Bijjahalli, S., Sabatini, R., & Gardi, A. (2020) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2020.100617 · Cited by 123

On generating trustworthy counterfactual explanations

Del Ser, J., Barredo-Arrieta, A., Díaz-Rodríguez, N., Herrera, F., Saranti, A., & Holzinger, A. (2024) · Information Sciences · doi:10.1016/j.ins.2023.119898 · Cited by 122

Weakly supervised learning of actions from transcripts

Kuehne, H., Richard, A., & Gall, J. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2017.06.004 · Cited by 119

Optical Flow based CNN for detection of unlearnt deepfake manipulations

Caldelli, R., Galteri, L., Amerini, I., & Del Bimbo, A. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.03.005 · Cited by 115

A survey on deep learning-based monocular spacecraft pose estimation: Current state, limitations and prospects

Pauly, L., Rharbaoui, W., Shneider, C., Rathinam, A., Gaudillière, V., & Aouada, D. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.08.001 · Cited by 121

HybridNet: A fast vehicle detection system for autonomous driving

Dai, X. (2019) · Signal Processing: Image Communication · doi:10.1016/j.image.2018.09.002 · Cited by 113

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification

Höhn, J., Krieghoff-Henning, E., Jutzi, T. B., von Kalle, C., Utikal, J. S., Meier, F., Gellrich, F. F., Hobelsberger, S., Hauschild, A., Schlager, J. G., et al. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.02.032 · Cited by 106

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects

Torrents-Barrena, J., Piella, G., Masoller, N., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2018.10.003 · Cited by 97

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

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

Satellite Pose Estimation Competition 2021: Results and Analyses

Park, T. H., Märtens, M., Jawaid, M., Wang, Z., Chen, B., Chin, T.-J., Izzo, D., & D’Amico, S. (2023) · Acta Astronautica · doi:10.1016/j.actaastro.2023.01.002 · Cited by 91

Benchmarking and scaling of deep learning models for land cover image classification

Papoutsis, I., Bountos, N. I., Zavras, A., Michail, D., & Tryfonopoulos, C. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.11.012 · Cited by 86

EL-YOLO: An efficient and lightweight low-altitude aerial objects detector for onboard applications

Xue, C., Xia, Y., Wu, M., Chen, Z., Cheng, F., & Yun, L. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124848 · Cited by 82

Improving random forests by neighborhood projection for effective text classification

Salles, T., Gonçalves, M., Rodrigues, V., & Rocha, L. (2018) · Information Systems · doi:10.1016/j.is.2018.05.006 · Cited by 66

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

Cloud-based scalable object detection and classification in video streams

Yaseen, M. U., Anjum, A., Rana, O., & Hill, R. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.02.003 · Cited by 55

Identifying associations among genomic, proteomic and imaging biomarkers via adaptive sparse multi-view canonical correlation analysis

Du, L., Zhang, J., Liu, F., Wang, H., Guo, L., Han, J., & Disease Neuroimaging Initiative, T. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102003 · Cited by 56

CNN architecture comparison for radio galaxy classification

Becker, B., Vaccari, M., Prescott, M., & Grobler, T. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab325 · Cited by 50

CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images

Yang, P., Yin, X., Lu, H., Hu, Z., Zhang, X., Jiang, R., & Lv, H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102539 · Cited by 50

Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system

Gao, R., Yuan, X., Ma, Y., Wei, T., Johnston, L., Shao, Y., Lv, W., Zhu, T., Zhang, Y., Zheng, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101536 · Cited by 45

Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound

Torrents-Barrena, J., Monill, N., Piella, G., Gratacós, E., Eixarch, E., Ceresa, M., & González Ballester, M. A. (2021) · Academic Radiology · doi:10.1016/j.acra.2019.11.006 · Cited by 42

Gender and texture classification: A comparative analysis using 13 variants of local binary patterns

Hadid, A., Ylioinas, J., Bengherabi, M., Ghahramani, M., & Taleb-Ahmed, A. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.04.017 · Cited by 41

Multi-level transfer learning for improving the performance of deep neural networks: Theory and practice from the tasks of facial emotion recognition and named entity recognition

Hung, J. C., & Chang, J.-W. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107491 · Cited by 35

On ground validation of debris removal technologies

Colmenarejo, P., Graziano, M., Novelli, G., Mora, D., Serra, P., Tomassini, A., Seweryn, K., Prisco, G., & Fernandez, J. G. (2019) · Acta Astronautica · doi:10.1016/j.actaastro.2018.01.026 · Cited by 33

Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations

Wilde, J., Serjeant, S., Bromley, J. M., Dickinson, H., Koopmans, L. V. E., & Metcalf, R. B. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac562 · Cited by 31

Hybrid deep learning model using SPCAGAN augmentation for insider threat analysis

R.G., G., Sajjanhar, A., & Xiang, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123533 · Cited by 28

A technical survey on statistical modelling and design methods for crowdsourcing quality control

Jin, Y., Carman, M., Zhu, Y., & Xiang, Y. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103351 · Cited by 28

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors

Xu, G., Wang, Q., & Ma, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab529 · Cited by 24

A comprehensive survey and taxonomy on privacy-preserving deep learning

Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21

Explainable AI in human motion: A comprehensive approach to analysis, modeling, and generation

Olivas-Padilla, B. E., Manitsaris, S., & Glushkova, A. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110418 · Cited by 19

Elongational rheology of bacterial biofilms in situ

Grumbein, S., Werb, M., Opitz, M., & Lieleg, O. (2016) · Journal of Rheology · doi:10.1122/1.4958667 · Cited by 19

Quantitative analysis of automatic voice disorder detection studies for hybrid feature and classifier selection

Lee, J. B., & Lee, H. G. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106014 · Cited by 15

A novel detection fusion network for solid waste sorting

Yu, Y., Zou, S., & Yin, K. (2020) · International Journal of Advanced Robotic Systems · doi:10.1177/1729881420941779 · Cited by 11

Residual Grounding Transformer Network for Terrain Recognition on the Lunar Surface

Qiu, L., Li, H., Li, Z., & Wang, C. (2021) · Applied Optics · doi:10.1364/ao.428232 · Cited by 7

Cross-view motion consistent self-supervised video inter-intra contrastive for action representation understanding

Bi, S., Hu, Z., Zhang, H., Di, J., & Sun, Z. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106578 · Cited by 3