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Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields.
Evolutionary robotics and the radical envelope-of-noise hypothesis
Jakobi, N.: · 1997
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Perlin, K.: · 2002
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Design and use paradigms for gazebo, an open-source multi-robot simulator
Koenig, N., Howard, A.: · 2004
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Microsoft COCO: Common objects in context
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: · 2014
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Unsupervised feature learning for 3d scene labeling
Lai, K., Bo, L., Fox, D.: · 2014
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Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: · 2015
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Visual domain adaptation: A survey of recent advances
Patel, V.M., Gopalan, R., Li, R., Chellappa, R.: · 2015
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R-fcn: Object detection via region-based fully convolutional networks
Dai, J., Li, Y., He, K., Sun, J.: · 2016
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SSD: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
Earlier work this paper cites.
Multiview RGB-D dataset for object instance detection
Georgakis, G., Reza, M.A., Mousavian, A., Le, P.H., Košecká, J.: · 2016
Earlier work this paper cites.
Multiview RGB-D dataset for object instance detection
Georgakis, G., Reza, M.A., Mousavian, A., Le, P., Kosecka, J.: · 2016
Cited alongside, same era.
YOLO9000: Better, faster, stronger
Redmon, J., Farhadi, A.: · 2017
Cited alongside, same era.
Sˆ 3fd: Single shot scale-invariant face detector
Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., Li, S.Z.: · 2017
Cited alongside, same era.
Interactive data collection for deep learning object detectors on humanoid robots
Maiettini, E., Pasquale, G., Rosasco, L., Natale, L.: · 2017
Cited alongside, same era.
Automatic localization of casting defects with convolutional neural networks
Ferguson, M., Ak, R., Lee, Y.T.T., Law, K.H.: · 2017
Cited alongside, same era.
Speed/accuracy trade-offs for modern convolutional object detectors
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., et al.: · 2017
Learning from simulated and unsupervised images through adversarial training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task
James, S., Davison, A.J., Johns, E.: · 2017
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Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification
Zhu, W., Liu, C., Fan, W., Xie, X.: · 2018
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Road damage detection using deep neural networks with images captured through a smartphone
Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T., Omata, H.: · 2018
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Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., Abbeel, P.: · 2017
Cited alongside, same era.
Deep convolutional neural networks for image classification: A comprehensive review
Rawat, W., Wang, Z.: · 2017
Cited alongside, same era.
Mask R-CNN
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
Cited alongside, same era.
Driving in the matrix: Can virtual worlds replace human-generated annotations for real world tasks?
Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S.N., Rosaen, K., Vasudevan, R.: · 2017
Cited alongside, same era.
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Zhu, Y., Mottaghi, R., Kolve, E., Lim, J.J., Gupta, A., Fei-Fei, L., Farhadi, A.: · 2017
Cited alongside, same era.
Closest in time.
The inaturalist species classification and detection dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: · 2018
Closest in time.
Taskonomy: Disentangling task transfer learning
Zamir, A.R., Sax, A., Shen, W., Guibas, L., Malik, J., Savarese, S.: · 2018
Closest in time.
Detectron
Girshick, R., Radosavovic, I., Gkioxari, G., Dollár, P., He, K.: · 2018
Closest in time.
A generic visual perception domain randomisation framework for gazebo
Borrego, J., Figueiredo, R., Dehban, A., Moreno, P., Bernardino, A., Santos-Victor, J.: · 2018
Closest in time.
Training deep networks with synthetic data: Bridging the reality gap by domain randomization
Tremblay, J., Prakash, A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon, S., Birchfield, S.: · 2018
Closest in time.