2013 IEEE International Conference on Computer Vision

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-López, S., Molina, D., Benjamins, R., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.10045 · Cited by 11,321

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423

Activation functions in deep learning: a comprehensive survey and benchmark

Dubey, S. R., Singh, S. K., & Chaudhuri, B. B. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.111 · Cited by 1,259

Predicting cancer outcomes from histology and genomics using convolutional networks

Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084

Measuring human perceptions of a large-scale urban region using machine learning

Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.08.020 · Cited by 810

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Saxena, D., & Cao, J. (2022) · ACM Computing Surveys · doi:10.1145/3446374 · Cited by 689

Automated EEG-based screening of depression using deep convolutional neural network

Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H., & Subha, D. P. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.012 · Cited by 650

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

Least squares 3D surface and curve matching

Gruen, A., & Akca, D. (2005) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2005.02.006 · Cited by 536

Weighted boxes fusion: Ensembling boxes from different object detection models

Solovyev, R., Wang, W., & Gabruseva, T. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104117 · Cited by 543

Monocular‐SLAM–based navigation for autonomous micro helicopters in GPS‐denied environments

Weiss, S., Scaramuzza, D., & Siegwart, R. (2011) · Journal of Field Robotics · doi:10.1002/rob.20412 · Cited by 414

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection

Cozzolino, D., Poggi, G., & Verdoliva, L. (2017) · Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security · doi:10.1145/3082031.3083247 · Cited by 349

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

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

Deep learning in robotics: A review of recent research

Pierson, H. A., & Gashler, M. S. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1365009 · Cited by 331

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

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Nazir, S., Dickson, D. M., & Akram, M. U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106668 · Cited by 283

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

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

A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges

Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101587 · Cited by 212

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

Animal biometrics: quantifying and detecting phenotypic appearance

Kühl, H. S., & Burghardt, T. (2013) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2013.02.013 · Cited by 189

COVID-19 image classification using deep learning: Advances, challenges and opportunities

Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168

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

Toward AI fashion design: An Attribute-GAN model for clothing match

Liu, L., Zhang, H., Ji, Y., & Jonathan Wu, Q. M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.011 · Cited by 141

Multi-task learning for quality assessment of fetal head ultrasound images

Lin, Z., Li, S., Ni, D., Liao, Y., Wen, H., Du, J., Chen, S., Wang, T., & Lei, B. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101548 · Cited by 129

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

Mixing up contrastive learning: Self-supervised representation learning for time series

Wickstrøm, K., Kampffmeyer, M., Mikalsen, K. Ø., & Jenssen, R. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.02.007 · Cited by 113

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

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

Unsupervised learning of invariant representations

Anselmi, F., Leibo, J. Z., Rosasco, L., Mutch, J., Tacchetti, A., & Poggio, T. (2016) · Theoretical Computer Science · doi:10.1016/j.tcs.2015.06.048 · Cited by 88

Joint learning of image detail and transmission map for single image dehazing

Zhang, S., He, F., Ren, W., & Yao, J. (2020) · The Visual Computer · doi:10.1007/s00371-018-1612-9 · Cited by 87

Detecting fake images by identifying potential texture difference

Yang, J., Xiao, S., Li, A., Lan, G., & Wang, H. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.06.043 · Cited by 86

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

Digital twin-based intelligent fish farming with Artificial Intelligence Internet of Things (AIoT)

Ubina, N. A., Lan, H.-Y., Cheng, S.-C., Chang, C.-C., Lin, S.-S., Zhang, K.-X., Lu, H.-Y., Cheng, C.-Y., & Hsieh, Y.-Z. (2023) · Smart Agricultural Technology · doi:10.1016/j.atech.2023.100285 · Cited by 82

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

Autonomous relative navigation around uncooperative spacecraft based on a single camera

Pesce, V., Opromolla, R., Sarno, S., Lavagna, M., & Grassi, M. (2019) · Aerospace Science and Technology · doi:10.1016/j.ast.2018.11.042 · Cited by 80

Domain adaptation from daytime to nighttime: A situation-sensitive vehicle detection and traffic flow parameter estimation framework

Li, J., Xu, Z., Fu, L., Zhou, X., & Yu, H. (2021) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2020.102946 · Cited by 80

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

Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting

Tong, T., Ledig, C., Guerrero, R., Schuh, A., Koikkalainen, J., Tolonen, A., Rhodius, H., Barkhof, F., Tijms, B., Lemstra, A. W., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.06.012 · Cited by 54

AI-based ICD coding and classification approaches using discharge summaries: A systematic literature review

Kaur, R., Ginige, J. A., & Obst, O. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118997 · Cited by 48

Estimating smile intensity: A better way

Girard, J. M., Cohn, J. F., & De la Torre, F. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.004 · Cited by 44

Passenger flow counting in buses based on deep learning using surveillance video

Hsu, Y.-W., Wang, T.-Y., & Perng, J.-W. (2020) · Optik · doi:10.1016/j.ijleo.2019.163675 · Cited by 43

RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease

Qiao, S., Pang, S., Luo, G., Pan, S., Yu, Z., Chen, T., & Lv, Z. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.10.001 · Cited by 42

SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal Network

Ma, S., Wei, Z., Tian, F., Fan, X., Zhang, J., Shen, X., Lin, Z., Huang, J., Měch, R., Samaras, D., et al. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300701 · Cited by 24

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