2017 IEEE International Conference on Computer Vision (ICCV)

Deep visual domain adaptation: A survey

Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209

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

PadChest: A large chest x-ray image dataset with multi-label annotated reports

Bustos, A., Pertusa, A., Salinas, J.-M., & de la Iglesia-Vayá, M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101797 · Cited by 691

Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery

Huang, B., Zhao, B., & Song, Y. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.04.050 · Cited by 591

Transparency of deep neural networks for medical image analysis: A review of interpretability methods

Salahuddin, Z., Woodruff, H. C., Chatterjee, A., & Lambin, P. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105111 · Cited by 508

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

Background subtraction in real applications: Challenges, current models and future directions

Garcia-Garcia, B., Bouwmans, T., & Rosales Silva, A. J. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2019.100204 · Cited by 337

Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks

Wurm, M., Stark, T., Zhu, X. X., Weigand, M., & Taubenböck, H. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.02.006 · Cited by 332

Analysis of the ISIC image datasets: Usage, benchmarks and recommendations

Cassidy, B., Kendrick, C., Brodzicki, A., Jaworek-Korjakowska, J., & Yap, M. H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2021.102305 · Cited by 307

Survey of computer vision algorithms and applications for unmanned aerial vehicles

Al-Kaff, A., Martín, D., García, F., Escalera, A. D. L., & María Armingol, J. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.033 · Cited by 246

AFEW-VA database for valence and arousal estimation in-the-wild

Kossaifi, J., Tzimiropoulos, G., Todorovic, S., & Pantic, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.02.001 · Cited by 236

Antibody structure prediction using interpretable deep learning

Ruffolo, J. A., Sulam, J., & Gray, J. J. (2022) · Patterns · doi:10.1016/j.patter.2021.100406 · Cited by 216

Deep learning for Alzheimer's disease diagnosis: A survey

Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102332 · Cited by 194

Transparency and accountability in AI decision support: Explaining and visualizing convolutional neural networks for text information

Kim, B., Park, J., & Suh, J. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113302 · Cited by 182

Deep learning-based unlearning of dataset bias for mri harmonisation and confound removal

Dinsdale, N. K., Jenkinson, M., & Namburete, A. I. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117689 · Cited by 178

Neural Rendering and Reenactment of Human Actor Videos

Liu, L., Xu, W., Zollhöfer, M., Kim, H., Bernard, F., Habermann, M., Wang, W., & Theobalt, C. (2019) · ACM Transactions on Graphics · doi:10.1145/3333002 · Cited by 163

Untitled work

(2020) · doi:10.1007/978-3-030-58586-0_27 · Cited by 156

IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition

Kwon, H., Tong, C., Haresamudram, H., Gao, Y., Abowd, G. D., Lane, N. D., & Plötz, T. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3411841 · Cited by 152

A comprehensive overview of Deepfake: generation, detection, datasets, and opportunities

Seow, J. W., Lim, M. K., Phan, R. C. W., & Liu, J. K. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.135 · Cited by 149

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

Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice

González-Gonzalo, C., Thee, E. F., Klaver, C. C. W., Lee, A. Y., Schlingemann, R. O., Tufail, A., Verbraak, F., & Sánchez, C. I. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2021.101034 · Cited by 103

Learning and inference on generative adversarial quantum circuits

Zeng, J., Wu, Y., Liu, J.-G., Wang, L., & Hu, J. (2019) · Physical Review A · doi:10.1103/physreva.99.052306 · Cited by 94

SpaceNet MVOI: A Multi-View Overhead Imagery Dataset

Weir, N., Lindenbaum, D., Bastidas, A., Etten, A., Kumar, V., Mcpherson, S., Shermeyer, J., & Tang, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00108 · Cited by 95

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded

Ozyoruk, K. B., Can, S., Darbaz, B., Başak, K., Demir, D., Gokceler, G. I., Serin, G., Hacisalihoglu, U. P., Kurtuluş, E., Lu, M. Y., et al. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00952-9 · 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

Vision based pixel-level bridge structural damage detection using a link ASPP network

Deng, W., Mou, Y., Kashiwa, T., Escalera, S., Nagai, K., Nakayama, K., Matsuo, Y., & Prendinger, H. (2020) · Automation in Construction · doi:10.1016/j.autcon.2019.102973 · Cited by 84

Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance

Barragán-Montero, A. M., Thomas, M., Defraene, G., Michiels, S., Haustermans, K., Lee, J. A., & Sterpin, E. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.026 · Cited by 69

In a Silent Way: Communication Between AI and Improvising Musicians Beyond Sound

McCormack, J., Gifford, T., Hutchings, P., Llano Rodriguez, M. T., Yee-King, M., & d'Inverno, M. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300268 · Cited by 64

A scoping review of fair machine learning techniques when using real-world data

Huang, Y., Guo, J., Chen, W.-H., Lin, H.-Y., Tang, H., Wang, F., Xu, H., & Bian, J. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104622 · Cited by 61

Stacked Memory Network for Video Summarization

Wang, J., Wang, W., Wang, Z., Wang, L., Feng, D., & Tan, T. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3350992 · Cited by 60

STDnet-ST: Spatio-temporal ConvNet for small object detection

Bosquet, B., Mucientes, M., & Brea, V. M. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.107929 · Cited by 57

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

Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

Ullah, A., & Dral, P. O. (2022) · Nature Communications · doi:10.1038/s41467-022-29621-w · Cited by 51

Approaching the Real-World: Supporting Activity Recognition Training with Virtual IMU Data

Kwon, H., Wang, B., Abowd, G. D., & Plötz, T. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3478096 · Cited by 48

Modality-transfer generative adversarial network and dual-level unified latent representation for visible thermal Person re-identification

Fan, X., Jiang, W., Luo, H., & Mao, W. (2022) · The Visual Computer · doi:10.1007/s00371-020-02015-z · Cited by 45

CrowdVerge: Predicting If People Will Agree on the Answer to a Visual Question

Gurari, D., & Grauman, K. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025781 · Cited by 45

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

A single-shot multi-level feature reused neural network for object detection

Wei, L., Cui, W., Hu, Z., Sun, H., & Hou, S. (2021) · The Visual Computer · doi:10.1007/s00371-019-01787-3 · Cited by 29

Robust Vision-Based Cheat Detection in Competitive Gaming

Jonnalagadda, A., Frosio, I., Schneider, S., McGuire, M., & Kim, J. (2021) · Proceedings of the ACM on Computer Graphics and Interactive Techniques · doi:10.1145/3451259 · Cited by 23

CoNNa–Hardware accelerator for compressed convolutional neural networks

Struharik, R. J. R., Vukobratović, B. Z., Erdeljan, A. M., & Rakanović, D. M. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.102991 · Cited by 18

A deep learning-based interactive medical image segmentation framework with sequential memory

Mikhailov, I., Chauveau, B., Bourdel, N., & Bartoli, A. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108038 · Cited by 15

Method for detection of unsafe actions in power field based on edge computing architecture

Yin, Y., Lin, J., Sun, N., Zhu, Q., Zhang, S., Zhang, Y., & Liu, M. (2021) · Journal of Cloud Computing · doi:10.1186/s13677-021-00234-w · Cited by 14

CyberCortex.AI: an AI-based operating system for autonomous robotics and complex automation

Grigorescu, S., & Zaha, M. (2025) · Journal of Field Robotics · doi:10.1002/rob.22426 · Cited by 9

Transfer learning for multimodal dialog

Palaskar, S., Sanabria, R., & Metze, F. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101093 · Cited by 6