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The state-of-the-art performance for object detection has been significantly improved over the past two years.
A. Cavallaro, O. Steiger, and T. Ebrahimi, “Tracking video objects in cluttered background,” TCSVT , 2005
2005
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E. Maggio, F. Smerladi, and A. Cavallaro, “Adaptive multifeature tracking in a particle filtering framework,” TCSVT , 2007
2007
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E. Maggio, M. Taj, and A. Cavallaro, “Efficient multitarget visual tracking using random finite sets,” TCSVT , 2008
2008
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J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” CVPR , 2009
2009
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J. Yang, D. Schonfeld, and M. Mohamed, “Robust video stabilization based on particle filter tracking of projected camera motion,” TCSVT , 2009
2009
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T. Deselaers, B. Alexe, and V. Ferrari, “Localizing Objects While Learning Their Appearance,” ECCV , 2010
2010
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A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari, “Learning object class detectors from weakly annotated video,” CVPR , 2012
2012
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” NIPS , 2012
2012
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2013
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A. Papazoglou and V. Ferrari, “Fast Object Segmentation in Unconstrained Video,” ICCV , 2013
2013
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M. Rubinstein, A. Joulin, J. Kopf, and C. Liu, “Unsupervised joint object discovery and segmentation in internet images,” in CVPR , 2013
2013
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J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” IJCV , 2013
2013
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K. Kang, W. Ouyang, H. Li, and X. Wang, “Object Detection from Video Tubelets with Convolutional Neural Networks,” in Computer Vision and Pattern Recognition , 2016
2013
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K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Int’l Conf. Learning Representations , 2014
2014
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R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” CVPR , 2014
2014
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D. Oneata, J. Revaud, J. Verbeek, and C. Schmid, “Spatio-temporal Object Detection Proposals,” ECCV , 2014
2014
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D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov, “Scalable object detection using deep neural networks,” 2014
2014
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A. Joulin, K. Tang, and L. Fei-Fei, “Efficient Image and Video Co-localization with Frank-Wolfe Algorithm,” ECCV , 2014
2014
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Two-Stream Convolutional Networks for Action Recognition in Videos,” in Conference on Neural Information Processing Systems , 2014
2014
Cited alongside, same era.
H. Possegger, T. Mauthner, P. M. Roth, and H. Bischof, “Occlusion geodesics for online multi-object tracking,” CVPR , 2014
2014
Cited alongside, same era.
C. L. Zitnick and P. Dollar, “Edge Boxes: Locating Object Proposals from Edges,” ECCV , 2014
2014
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” CVPR , 2015
2015
Cited alongside, same era.
Y. Li, J. Zhu, and S. C. Hoi, “Reliable patch trackers: Robust visual tracking by exploiting reliable patches,” CVPR , 2015
2015
Later among the works it cites.
Z. Hong, Z. Chen, C. Wang, X. Mei, D. Prokhorov, and D. Tao, “Multi-store tracker (muster): a cognitive psychology inspired approach to object tracking,” CVPR , 2015
2015
Later among the works it cites.
L. Wang, W. Ouyang, X. Wang, and H. Lu, “Visual tracking with fully convolutional networks,” ICCV , 2015
2015
Later among the works it cites.
2015
Later among the works it cites.
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R. Girshick, “Fast r-cnn,” ICCV , 2015
2015
Cited alongside, same era.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” NIPS , 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
W. Ouyang, X. Wang, X. Zeng, S. Qiu, P. Luo, Y. Tian, H. Li, S. Yang, Z. Wang, C.-C. Loy et al. , “DeepID-net: Deformable deep convolutional neural networks for object detection,” CVPR , 2015
2015
Cited alongside, same era.
S. Gidaris and N. Komodakis, “Object detection via a multi-region and semantic segmentation-aware cnn model,” in ICCV , 2015
2015
Cited alongside, same era.
R. Girshick, F. Iandola, T. Darrell, and J. Malik, “Deformable part models are convolutional neural networks,” in CVPR , 2015
2015
Cited alongside, same era.
D. Mrowca, M. Rohrbach, J. Hoffman, R. Hu, K. Saenko, and T. Darrell, “Spatial semantic regularisation for large scale object detection,” in ICCV , 2015
2015
Cited alongside, same era.
2015
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Closest in time.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in CVPR , 2016
2016
Closest in time.
K. Wang, L. Lin, W. Zuo, S. Gu, and L. Zhang, “Dictionary pair classifier driven convolutional neural networks for object detection,” in CVPR , 2016, pp. 2138–2146
2016
Closest in time.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in ECCV . Springer, 2016, pp. 21–37
2016
Closest in time.
2016
Closest in time.
K. Wang, D. Zhang, Y. Li, R. Zhang, and L. Lin, “Cost-effective active learning for deep image classification,” TCSVT , 2016
2016
Closest in time.
B. Yang, J. Yan, Z. Lei, and S. Z. Li, “Craft objects from images,” CVPR , 2016
2016
Closest in time.
X. Zeng, W. Ouyang, B. Yang, J. Yan, and X. Wang, “Gated bi-directional cnn for object detection,” in ECCV . Springer, 2016, pp. 354–369
2016
Closest in time.
2016
Closest in time.
L. Galteri, L. Seidenari, M. Bertini, and A. Del Bimbo, “Spatio-temporal closed-loop object detection,” IEEE Transactions on Image Processing , 2017
2017
Closest in time.
K. Kang, H. Li, T. Xiao, W. Ouyang, J. Yan, X. Liu, and X. Wang, “Object detection in videos with tubelet proposal networks,” CVPR , 2017
2017
Closest in time.
C. Li, L. Lin, W. Zuo, and J. Tang, “Learning patch-based dynamic graph for visual tracking.” in AAAI , 2017, pp. 4126–4132
2017
Closest in time.