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In vision-enabled autonomous systems such as robots and autonomous cars, video object detection plays a crucial role, and both its speed and accuracy are important factors to provide reliable operation.
Scalable object detection using deep neural networks
Erhan, D., Szegedy, C., Toshev, A., and Anguelov, D · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2014
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Locally scale-invariant convolutional neural networks
Kanazawa, A., Sharma, A., and Jacobs, D · 2014
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., and Zhang, Z · 2015
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Fast r-cnn
Girshick, R · 2015
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Bridges: A uniquely flexible hpc resource for new communities and data analytics
Nystrom, N. A., Levine, M. J., Roskies, R. Z., and Scott, J. R · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and 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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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
Bell, S., Lawrence Zitnick, C., Bala, K., and Girshick, R · 2016
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A unified multi-scale deep convolutional neural network for fast object detection
Cai, Z., Fan, Q., Feris, R. S., and Vasconcelos, N · 2016
Cited alongside, same era.
R-fcn: Object detection via region-based fully convolutional networks
Dai, J., Li, Y., He, K., and Sun, J · 2016
Cited alongside, same era.
Seq-nms for video object detection
Han, W., Khorrami, P., Paine, T. L., Ramachandran, P., Babaeizadeh, M., Shi, H., Li, J., Yan, S., and Huang, T. S · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C · 2016
Cited alongside, same era.
Deformable convolutional networks
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y · 2017
Recurrent scale approximation for object detection in cnn
Liu, Y., Li, H., Yan, J., Wei, F., Wang, X., and Tang, X · 2017
Later among the works it cites.
Youtube-boundingboxes: A large high-precision human-annotated data set for object detection in video
Real, E., Shlens, J., Mazzocchi, S., Pan, X., and Vanhoucke, V · 2017
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Yolo9000: Better, faster, stronger
Redmon, J. and Farhadi, A · 2017
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Object detection networks on convolutional feature maps
Ren, S., He, K., Girshick, R., Zhang, X., and Sun, J · 2017
Later among the works it cites.
Cad: Scale invariant framework for real-time object detection
Zhou, H., Li, Z., Ning, C., and Tang, J · 2017
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Eva²: Exploiting temporal redundancy in live computer vision
Buckler, M., Bedoukian, P., Jayasuriya, S., and Sampson, A · 2018
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Cited alongside, same era.
Detect to track and track to detect
Feichtenhofer, C., Pinz, A., and Zisserman, A · 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
Cited alongside, same era.
T-cnn: Tubelets with convolutional neural networks for object detection from videos
Kang, K., Li, H., Yan, J., Zeng, X., Yang, B., Xiao, T., Zhang, C., Wang, Z., Wang, R., Wang, X., et al · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S
Cited in the paper.
Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollar, P
Cited in the paper.
Flow-guided feature aggregation for video object detection
Zhu, X., Wang, Y., Dai, J., Yuan, L., and Wei, Y
Cited in the paper.
Later among the works it cites.
Domain-specific approximation for object detection
Chin, T. W., Yu, C. L., Halpern, M., Genc, H., Tsao, S. L., and Reddi, V. J · 2018
Later among the works it cites.
Euphrates: Algorithm-soc co-design for low-power mobile continuous vision
Zhu, Y., Samajdar, A., Mattina, M., and Whatmough, P · 2018
Later among the works it cites.