2011 International Conference on Computer Vision

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762

Diffusion Models: A Comprehensive Survey of Methods and Applications

Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704

Deep Learning on Image Denoising: An overview

Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C.-W. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.07.025 · Cited by 1,091

Vehicle detection in aerial imagery : A small target detection benchmark

Razakarivony, S., & Jurie, F. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.11.002 · Cited by 916

Human motion tracking for rehabilitation-A survey

Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M., Vaillant, G., Lee, A. M., Aung, N., Lukaschuk, E., Sanghvi, M. M., et al. (2018) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/s12968-018-0471-x · Cited by 773

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

Attention-guided CNN for image denoising

Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., & Liu, H. (2020) · Neural Networks · doi:10.1016/j.neunet.2019.12.024 · Cited by 688

Two years of Visual Odometry on the Mars Exploration Rovers

Maimone, M., Cheng, Y., & Matthies, L. (2007) · Journal of Field Robotics · doi:10.1002/rob.20184 · Cited by 655

Texture optimization for example-based synthesis

Kwatra, V., Essa, I., Bobick, A., & Kwatra, N. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073263 · Cited by 608

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

Deep learning-based image recognition for autonomous driving

Fujiyoshi, H., Hirakawa, T., & Yamashita, T. (2019) · IATSS Research · doi:10.1016/j.iatssr.2019.11.008 · Cited by 439

Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar

Tolan, J., Yang, H.-I., Nosarzewski, B., Couairon, G., Vo, H. V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., et al. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113888 · Cited by 419

Understanding the role of individual units in a deep neural network

Bau, D., Zhu, J.-Y., Strobelt, H., Lapedriza, A., Zhou, B., & Torralba, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907375117 · Cited by 375

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

Anipose: A toolkit for robust markerless 3D pose estimation

Karashchuk, P., Rupp, K. L., Dickinson, E. S., Walling-Bell, S., Sanders, E., Azim, E., Brunton, B. W., & Tuthill, J. C. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109730 · Cited by 324

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

A survey of methods for 3D histology reconstruction

Pichat, J., Iglesias, J. E., Yousry, T., Ourselin, S., & Modat, M. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.004 · Cited by 207

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

Automated emotion recognition: Current trends and future perspectives

Maithri, M., Raghavendra, U., Gudigar, A., Samanth, J., Barua, P. D., Murugappan, M., Chakole, Y., & Acharya, U. R. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106646 · Cited by 172

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

Detecting animals in African Savanna with UAVs and the crowds

Rey, N., Volpi, M., Joost, S., & Tuia, D. (2017) · Remote Sensing of Environment · doi:10.1016/j.rse.2017.08.026 · Cited by 150

A barcode shape descriptor for curve point cloud data

Collins, A., Zomorodian, A., Carlsson, G., & Guibas, L. J. (2004) · Computers & Graphics · doi:10.1016/j.cag.2004.08.015 · Cited by 132

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

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

Survey on Explainable AI: Techniques, challenges and open issues

Abusitta, A., Li, M. Q., & Fung, B. C. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124710 · Cited by 98

Prognostics of Lithium-Ion batteries using knowledge-constrained machine learning and Kalman filtering

Bai, G., Su, Y., Rahman, M. M., & Wang, Z. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108944 · Cited by 76

Automation of 3D reconstruction of neural tissue from large volume of conventional serial section transmission electron micrographs

Mishchenko, Y. (2009) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.09.006 · Cited by 73

Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module

Liu, Y., Yuan, Z., Mao, H., Liang, Z., Yang, W., Qiu, Y., Cheng, T., Li, X., Xu, H., & Gao, K. (2022) · Proceedings of the 2022 International Conference on Multimodal Interaction · doi:10.1145/3536221.3556630 · Cited by 64

MRI-to-PET cross-modality translation using globally & locally aware GAN (GLA-GAN) for multi-modal diagnosis of Alzheimer’s disease

Sikka, A., Peri, S., Virk, J. S., Niyaz, U., & Bathula, D. R. (2025) · The Journal of Precision Medicine: Health and Disease · doi:10.1016/j.premed.2025.100004 · Cited by 58

A survey on hardware security of DNN models and accelerators

Mittal, S., Gupta, H., & Srivastava, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102163 · Cited by 47

Videoscope-based inspection of turbofan engine blades using convolutional neural networks and image processing

Kim, Y.-H., & Lee, J.-R. (2019) · Structural Health Monitoring · doi:10.1177/1475921719830328 · Cited by 44

One step further into the blackbox: A pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound

Dong, F., She, R., Cui, C., Shi, S., Hu, X., Zeng, J., Wu, H., Xu, J., & Zhang, Y. (2021) · European Radiology · doi:10.1007/s00330-020-07561-7 · Cited by 43

The power of paired proximity science observations: Co-located data from SHERLOC and PIXL on Mars

Razzell Hollis, J., Moore, K. R., Sharma, S., Beegle, L., Grotzinger, J. P., Allwood, A., Abbey, W., Bhartia, R., Brown, A. J., Clark, B., et al. (2022) · Icarus · doi:10.1016/j.icarus.2022.115179 · Cited by 37

Triple Robustness Augmentation Local Features for multi-source image registration

Wang, C., Xu, L., Xu, R., Xu, S., Meng, W., Wang, R., & Zhang, X. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2023.03.023 · Cited by 22

Video-based automatic people counting for public transport: On-bus versus off-bus deployment

McCarthy, C., Ghaderi, H., Martí, F., Jayaraman, P., & Dia, H. (2025) · Computers in Industry · doi:10.1016/j.compind.2024.104195 · Cited by 14

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

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