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Object detection has been vigorously investigated for years but fast accurate detection for real-world scenes remains a very challenging problem.
Z. Shen, Z. Liu, J. Li, Y. Jiang, Y. Chen, and X. Xue. “DSOD: Learning deeply supervised object detectors from scratch,” in Proc. IEEE Int. Conf. Comput. Vis. , Venice, Italy, Oct. 2017, pp. 1919–1927
1927
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,” Int. J. Comput. Vis. , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
T. Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Proc. Eur. Conf. Comput. Vis. , Zurich, Switzerland, Sep. 2014, pp. 740–755, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” in Proc. Adv. Neural Info. Process Syst. , Montreal, Canada, Dec. 2015, pp. 91–99
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and F. Li, “ImageNet large scale visual recognition challenge,” Int. J. Comput. Vis. , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Boston, USA, Jun. 2015, pp. 1–9
2015
Earlier work this paper cites.
S. Ioffe, and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proc. Int. Conf. Machine Learn. , Lile, France, Jul. 2015, pp. 448–456
2015
Earlier work this paper cites.
J. Dai, Y. Li, K. He, and J. Sun, “R-FCN: Object detection via region-based fully convolutional networks,” in Proc. Adv. Neural Info. Process Syst. , Barcelona, Spain, Dec. 2016, pp. 379–387
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Las Vegas, USA, Jun. 2016, pp. 779–788
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in Proc. Eur. Conf. Comput. Vis. , Amsterdam, Netherlands, Oct. 2016, pp. 21–37
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Las Vegas, USA, Jun. 2016, pp. 770–778
2016
Earlier work this paper cites.
W. Luo, Y. Li, R. Urtasun, and R. Zemel, “Understanding the effective receptive field in deep convolutional neural networks,” in Proc. Adv. Neural Info. Process Syst. , Barcelona, Spain, Dec. 2016, pp. 4898–4906
2016
Earlier work this paper cites.
C. Feichtenhofer, A. Pinz, and A. Zisserman, “Detect to track and track to detect,” in Proc. Int. Conf. Comput. Vis. , Venice, Italy, Oct. 2017, pp. 3038–3046
2017
Earlier work this paper cites.
X. Zhu, Y. Wang, J. Dai, L. Yuan, and Y. Wei, “Flow-guided feature aggregation for video object detection,” in Proc. Int. Conf. Comput. Vis. , Venice, Italy, Oct. 2017, pp. 408–417
2017
Earlier work this paper cites.
K. Kang, H. Li, T. Xiao, W. Ouyang, J. Yan, X. Liu, and X. Wang, “Object detection in videos with tubelet proposal networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Honolulu, USA, Jul. 2017, pp. 727–735
2017
Cited alongside, same era.
Y. Zhu, C. Zhao, J. Wang, X. Zhao, Y. Wu, H. Lu, “Couplenet: Coupling global structure with local parts for object detection,” in Proc. IEEE Int. Conf. Comput. Vis. , Venice, Italy, Aug. 2017, pp. 4126–4134
2017
Cited alongside, same era.
T. Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, S. Belongie, “Feature pyramid networks for object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, Honolulu, USA, Jun. 2017, pp. 2117–2125
2017
Cited alongside, same era.
T. Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, “Focal loss for dense object detection,” in Proc. IEEE Int. Conf. Comput. Vis. , Venice, Italy, Oct. 2017, pp. 2980–2988
2017
Cited alongside, same era.
M. Liu and M. Zhu, “Mobile video object detection with temporally-aware feature maps,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Salt Lake City, USA, Jun. 2018, pp. 5686–5695
2018
Closest in time.
S. Liu, D. Huang, and Y. Wang, “Receptive field block net for accurate and fast object detection,” in Proc. Eur. Conf. Comput. Vis. , Munich, Germany, Sep. 2018, pp. 404–419
2018
Closest in time.
J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,” arXiv:1804.02767 , 2018
2018
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H. Law and J. Deng, “CornerNet: Detecting objects as paired keypoints,” in Proc. Eur. Conf. Comput. Vis. , Munich, Germany, Sep. 2018, pp. 734–750
2018
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H. Hu, J. Gu, Z. Zhang, J. Dai, Y. Wei, “Relation networks for object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Salt Lake City, USA, Jun. 2018, pp. 3588–3597
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2017
Cited alongside, same era.
J. Redmon and A. Farhadi, “YOLO9000: Better, faster, stronger,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Honolulu, USA, Jul., 2017, pp. 6517–6525
2017
Cited alongside, same era.
Y. Pang, J. Cao, and X. Li, “Learning sampling distributions for efficient object detection,” IEEE Trans. Cybern., vol. 47, no. 1, pp. 117–129, 2017
2017
Cited alongside, same era.
J. Dai H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks”, in Proc. Int. Conf. Comput. Vis. , Venice, Italy, Oct. 2017, pp.764–773
2017
Cited alongside, same era.
G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition, Honolulu, USA, Jun. 2017, pp. 4700–4708
2017
Cited alongside, same era.
2017
Cited alongside, same era.
S. Zhang, L. Wen, X. Bian, Z. Lei, and S. Z. Li, “Single-shot refinement neural network for object detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Salt Lake City, USA, Jun. 2018, pp. 4203–4212
2018
Cited alongside, same era.
K. Kang, H. Li, J. Yan, X. Zeng, B. Yang, T. Xiao, C. Zhang, Z. Wang, R. Wang, X. Wang, and X. Ouyang, “T-CNN: Tubelets with convolutional neural networks for object detection from videos”, IEEE Trans. Circuits Syst. Video Technol. , vol. 28, no. 10, pp. 2896–2907, 2018
2018
Cited alongside, same era.
2018
Closest in time.
Z. Zhao, P. Zheng, S. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 30, no. 11, pp. 3212–3232, 2019
2019
Closest in time.
X. Chen, J. Yu, and Z. Wu, “Temporally identity-aware SSD with attentional LSTM,” IEEE Trans. Cybern. , to be published, doi: 10.1109/TCYB.2019.2894261
2019
Closest in time.
Y. Zhu, C. Zhao, H. Guo, J. Wang, X. Zhao, and H. Lu, “Attention couplenet: Fully convolutional attention coupling network for object detection,” IEEE Trans. Image Process., vol. 28, no. 1, pp. 113–126, 2019
2019
Closest in time.
X. Zhou, J. Zhuo, and P. Krähenbühl, “Bottom-up object detection by grouping extreme and center points,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Long Beach, USA, Jun. 2019, pp. 850–859
2019
Closest in time.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” arXiv:1904.07850 , 2019
2019
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2019
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H. Zhang, H. Chang, B. Ma, S. Shan, X. Chen, “Cascade retinanet: Maintaining consistency for single-stage object detection,” in Proc. British Machine Vision Conference , Cardiff, UK, Sep. 2019, pp. 1–12
2019
Closest in time.
J. Wang, K. Chen, S. Yang, C. C. Loy, and D. Lin, “Region proposal by guided anchoring,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition , Long Beach, USA, 2019, pp. 2965–2974
2019
Closest in time.
C. Chi, S. Zhang, J. Xing, Z. Lei, S. Z. Li, and X. Zou, “Selective refinement network for high performance face detection,” in Proc. AAAI Conf. Artifical Intell. , Honolulu, USA, Jul. 2019, pp. 8231–8238
2019
Closest in time.
A. Singha and M. K. Bhowmik, “Salient features for moving object detection in adverse weather conditions during night time,” IEEE Trans. Circuits Syst. Video Technol. , to be published, doi:10.1109/TCSVT.2019.2926164
2019
Closest in time.