Fetching the paper…
Reading the bibliography…
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g.
The recognition of human movement using temporal templates
A. F. Bobick and J. W. Davis · 2001
Earlier work this paper cites.
Learning object class detectors from weakly annotated video
A. Prest, C. Leistner, J. Civera, C. Schmid, and V. Ferrari · 2012
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment
J. Zhang, S. Shan, M. Kan, and X. Chen · 2014
Earlier work this paper cites.
Fast r-cnn
R. Girshick · 2015
Earlier work this paper cites.
Single image super-resolution from transformed self-exemplars
J.-B. Huang, A. Singh, and N. Ahuja · 2015
Earlier work this paper cites.
Watch and learn: Semi-supervised learning of object detectors from videos
I. Misra, A. Shrivastava, and M. Hebert · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
Face alignment by coarse-to-fine shape searching
S. Zhu, C. Li, C. C. Loy, and X. Tang · 2015
Earlier work this paper cites.
R-fcn: Object detection via region-based fully convolutional networks
J. Dai, Y. Li, K. He, and J. Sun · 2016
Earlier work this paper cites.
Seq-nms for video object detection
W. Han, P. Khorrami, T. L. Paine, P. Ramachandran, M. Babaeizadeh, H. Shi, J. Li, S. Yan, and T. S. Huang · 2016
Earlier work this paper cites.
Efficient coarse-to-fine patchmatch for large displacement optical flow
Y. Hu, R. Song, and Y. Li · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
Cited alongside, same era.
Object detection from video tubelets with convolutional neural networks
K. Kang, W. Ouyang, H. Li, and X. Wang · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Temporal segment networks: Towards good practices for deep action recognition
Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
Later among the works it cites.
Object detection in videos with tubelet proposal networks
K. Kang, H. Li, T. Xiao, W. Ouyang, J. Yan, X. Liu, and X. Wang · 2017
Later among the works it cites.
Deep laplacian pyramid networks for fast and accurate super-resolution
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 2017
Later among the works it cites.
Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade
X. Li, Z. Liu, P. Luo, C. C. Loy, and X. Tang · 2017
Later among the works it cites.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
Later among the works it cites.
Focal loss for dense object detection
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool · 2016
Cited alongside, same era.
Discover and learn new objects from documentaries
K. Chen, H. Song, C. C. Loy, and D. Lin · 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. Girshick · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Later among the works it cites.
Yolo9000: Better, faster, stronger
J. Redmon and A. Farhadi · 2017
Later among the works it cites.
Dsod: Learning deeply supervised object detectors from scratch
Z. Shen, Z. Liu, J. Li, Y.-G. Jiang, Y. Chen, and X. Xue · 2017
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, M. Lin, and J. Sun · 2017
Later among the works it cites.
Flow-guided feature aggregation for video object detection
X. Zhu, Y. Wang, J. Dai, L. Yuan, and Y. Wei · 2017
Later among the works it cites.
Deep feature flow for video recognition
X. Zhu, Y. Xiong, J. Dai, L. Yuan, and Y. Wei · 2017
Later among the works it cites.