LabelMe: A Database and Web-Based Tool for Image Annotation

Learning methods for generic object recognition with invariance to pose and lighting

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ImageNet classification with deep convolutional neural networks

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ImageNet: A large-scale hierarchical image database

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Microsoft COCO: Common Objects in Context

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ImageNet Large Scale Visual Recognition Challenge

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The pascal visual object classes (VOC) challenge

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SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631 · see citing works

Are we ready for autonomous driving? The KITTI vision benchmark suite

Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897 · see citing works

The Cityscapes Dataset for Semantic Urban Scene Understanding

Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433 · see citing works

The Pascal Visual Object Classes Challenge: A Retrospective

Everingham, M., Eslami, S. M. A., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-014-0733-5 · Cited by 7,139 · see citing works

Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works

Hmdb: a large video database for human motion recognition

Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965 · see citing works

Scene Parsing through ADE20K Dataset

Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.544 · Cited by 3,326 · see citing works

Deep Learning for Generic Object Detection: A Survey

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01247-4 · Cited by 2,965 · see citing works

Unbiased look at dataset bias

Torralba, A., & Efros, A. A. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995347 · Cited by 2,440 · see citing works

The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes

Ros, G., Sellart, L., Materzynska, J., Vazquez, D., & Lopez, A. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.352 · Cited by 2,338 · see citing works

Object detection in optical remote sensing images: A survey and a new benchmark

Li, K., Wan, G., Cheng, G., Meng, L., & Han, J. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.023 · Cited by 2,317 · see citing works

SUN RGB-D: A RGB-D scene understanding benchmark suite

Song, S., Lichtenberg, S. P., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298655 · Cited by 1,943 · see citing works

Semantic Understanding of Scenes Through the ADE20K Dataset

Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1140-0 · Cited by 1,831 · see citing works

Sparse Representation for Color Image Restoration

Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716 · see citing works

The Role of Context for Object Detection and Semantic Segmentation in the Wild

Mottaghi, R., Chen, X., Liu, X., Cho, N.-G., Lee, S.-W., Fidler, S., Urtasun, R., & Yuille, A. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.119 · Cited by 1,438 · see citing works

Image denoising: Can plain neural networks compete with BM3D?

Burger, H. C., Schuler, C. J., & Harmeling, S. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247952 · Cited by 1,325 · see citing works

LVIS: A Dataset for Large Vocabulary Instance Segmentation

Gupta, A., Dollar, P., & Girshick, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00550 · Cited by 1,274 · see citing works

Image retrieval using scene graphs

Johnson, J., Krishna, R., Stark, M., Li, L.-J., Shamma, D. A., Bernstein, M. S., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298990 · Cited by 1,101 · see citing works

The role of context in object recognition

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Poselets: Body part detectors trained using 3D human pose annotations

Bourdev, L., & Malik, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459303 · Cited by 961 · see citing works

Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna

Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., & Packer, C. (2015) · Scientific Data · doi:10.1038/sdata.2015.26 · Cited by 510 · see citing works

Recognition of natural scenes from global properties: seeing the forest without representing the trees

GREENE, M., & OLIVA, A. (2009) · Cognitive Psychology · doi:10.1016/j.cogpsych.2008.06.001 · Cited by 467 · see citing works

Commonsense reasoning and commonsense knowledge in artificial intelligence

Davis, E., & Marcus, G. (2015) · Communications of the ACM · doi:10.1145/2701413 · Cited by 434 · see citing works

Pathology image analysis using segmentation deep learning algorithms

Wang, S., Yang, D. M., Rong, R., Zhan, X., & Xiao, G. (2019) · The American Journal of Pathology · doi:10.1016/j.ajpath.2019.05.007 · Cited by 382 · see citing works

NeuFlow: A runtime reconfigurable dataflow processor for vision

Farabet, C., Martini, B., Corda, B., Akselrod, P., Culurciello, E., & LeCun, Y. (2011) · CVPR 2011 WORKSHOPS · doi:10.1109/cvprw.2011.5981829 · Cited by 369 · see citing works

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 · see citing works

On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID

Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250 · see citing works

Deep Learning for Generic Object Detection: A Survey

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965 · see citing works

Real-time apple detection system using embedded systems with hardware accelerators: an edge ai application

Mazzia, V., Khaliq, A., Salvetti, F., & Chiaberge, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964608 · Cited by 212 · see citing works

Generative AI in Higher Education: The ChatGPT Effect

Chan, C. K. Y., & Colloton, T. (2024) · Routledge · doi:10.4324/9781003459026 · Cited by 203 · see citing works

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 · see citing works

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Chen, L.-C., Lopes, R. G., Cheng, B., Collins, M. D., Cubuk, E. D., Zoph, B., Adam, H., & Shlens, J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58545-7_40 · Cited by 165 · see citing works

Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation

Lai, K., & Fox, D. (2010) · The International Journal of Robotics Research · doi:10.1177/0278364910369190 · Cited by 152 · see citing works

Stimulus Repetition Probability Does Not Affect Repetition Suppression in Macaque Inferior Temporal Cortex

Kaliukhovich, D. A., & Vogels, R. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq207 · Cited by 131 · see citing works

Visual Object Recognition

Grauman, K., & Leibe, B. (2011) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00332ed1v01y201103aim011 · Cited by 126 · see citing works

Polarization-driven semantic segmentation via efficient attention-bridged fusion

Xiang, K., Yang, K., & Wang, K. (2021) · Optics Express · doi:10.1364/oe.416130 · Cited by 111 · see citing works

Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force

Berzin, T. M., Parasa, S., Wallace, M. B., Gross, S. A., Repici, A., & Sharma, P. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.06.035 · Cited by 102 · see citing works

Reward guides attention to object categories in real-world scenes.

Hickey, C., Kaiser, D., & Peelen, M. V. (2015) · Journal of Experimental Psychology: General · doi:10.1037/a0038627 · Cited by 91 · see citing works

Traffic Scene Segmentation Based on RGB-D Image and Deep Learning

Li, L., Qian, B., Lian, J., Zheng, W., & Zhou, Y. (2018) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2017.2724138 · Cited by 77 · see citing works

The Shape Boltzmann Machine: A strong model of object shape

Eslami, S. M. A., Heess, N., & Winn, J. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247702 · Cited by 61 · see citing works

A review of current systems for annotation of cell and tissue images in digital pathology

Korzynska, A., Roszkowiak, L., Zak, J., & Siemion, K. (2021) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2021.04.012 · Cited by 26 · see citing works

Towards large-scale multimedia retrieval enriched by knowledge about human interpretation: Retrospective survey

Shirahama, K., & Grzegorzek, M. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2292-8 · Cited by 24 · see citing works

Investigating the Impact of Annotation Interfaces on Player Performance in Distributed Multiplayer Games

Alharthi, S. A., Torres, R. C., Khalaf, A. S., Toups Dugas, P. O., Dolgov, I., & Nacke, L. E. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173888 · Cited by 21 · see citing works

Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers

Kuang, B., Rana, Z. A., & Zhao, Y. (2021) · Sensors · doi:10.3390/s21216996 · Cited by 14 · see citing works

Deep Learning Approaches for Vehicle and Pedestrian Detection in Adverse Weather

Zaman, M., Saha, S., Zohrabi, N., & Abdelwahed, S. (2023) · 2023 IEEE Transportation Electrification Conference & Expo (ITEC) · doi:10.1109/itec55900.2023.10187020 · Cited by 13 · see citing works