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Recent advances in single-frame object detection and segmentation techniques have motivated a wide range of works to extend these methods to process video streams.
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A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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V. Lebedev, Y. Ganin, M. Rakhuba, I. Oseledets, and V. Lempitsky · 2014
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Leveling the playing field: Attention mitigates the effects of intelligence on memory
J. Markant and D. Amso · 2014
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Recurrent models of visual attention
V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu · 2014
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J. Ba, V. Mnih, and K. Kavukcuoglu · 2015
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Active object localization with deep reinforcement learning
J. C. Caicedo and S. Lazebnik · 2015
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Fast r-cnn
R. Girshick · 2015
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DRAW: A recurrent neural network for image generation
K. Gregor, I. Danihelka, A. Graves, and D. Wierstra · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015
S. Ioffe and C. Szegedy · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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R-FCN: object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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SSD: single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. E. Reed, C. Fu, and A. C. Berg · 2016
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A benchmark dataset and evaluation methodology for video object segmentation
F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung · 2016
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You only look once: Unified, real-time object detection
J. Redmon, S. K. Divvala, R. B. Girshick, and A. Farhadi · 2016
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Clockwork convnets for video semantic segmentation
E. Shelhamer, K. Rakelly, J. Hoffman, and T. Darrell · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Learning video object segmentation with visual memory
P. Tokmakov, K. Alahari, and C. Schmid · 2017
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Incremental network quantization: Towards lossless cnns with low-precision weights
A. Zhou, A. Yao, Y. Guo, L. Xu, and Y. Chen · 2017
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Object detection in video with spatiotemporal sampling networks
G. Bertasius, L. Torresani, and J. Shi · 2018
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Masklab: Instance segmentation by refining object detection with semantic and direction features
L. Chen, A. Hermans, G. Papandreou, F. Schroff, P. Wang, and H. Adam · 2018
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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Reinforcement cutting-agent learning for video object segmentation
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Deep reinforcement learning with double q-learning
H. van Hasselt, A. Guez, and D. Silver · 2016
Cited alongside, same era.
Segflow: Joint learning for video object segmentation and optical flow
J. Cheng, Y. Tsai, S. Wang, and M. Yang · 2017
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
F. Chollet · 2017
Cited alongside, same era.
Detect to track and track to detect
C. Feichtenhofer, A. Pinz, and A. Zisserman · 2017
Cited alongside, same era.
Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick · 2017
Cited alongside, same era.
Fusionseg: Learning to combine motion and appearance for fully automatic segmentation of generic objects in videos
S. D. Jain, B. Xiong, and K. Grauman · 2017
Cited alongside, same era.
J. Han, L. Yang, D. Zhang, X. Chang, and X. Liang · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. G. Howard, H. Adam, and D. Kalenichenko · 2018
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End-to-end policy learning for active visual categorization
D. Jayaraman and K. Grauman · 2018
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Low-latency video semantic segmentation
Y. Li, J. Shi, and D. Lin · 2018
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Mobile video object detection with temporally-aware feature maps
M. Liu and M. Zhu · 2018
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Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation
M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen · 2018
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Pyramid dilated deeper convlstm for video salient object detection
H. Song, W. Wang, S. Zhao, J. Shen, and K.-M. Lam · 2018
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Video object detection with an aligned spatial-temporal memory
F. Xiao and Y. J. Lee · 2018
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Towards high performance video object detection
X. Zhu, J. Dai, L. Yuan, and Y. Wei · 2018
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Towards high performance video object detection for mobiles
X. Zhu, J. Dai, X. Zhu, Y. Wei, and L. Yuan · 2018
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