Fetching the paper…
Reading the bibliography…
In recent years, we have seen tremendous progress in the field of object detection.
Feedforward, horizontal, and feedback processing in the visual cortex
V. A. Lamme, H. Super, and H. Spekreijse · 1998
Earlier work this paper cites.
Top-down attentional guidance based on implicit learning of visual covariation
M. M. Chun and Y. Jiang · 1999
Earlier work this paper cites.
The neural mechanisms of top-down attentional control
J. B. Hopfinger, M. H. Buonocore, and G. R. Mangun · 2000
Earlier work this paper cites.
The distinct modes of vision offered by feedforward and recurrent processing
V. A. Lamme and P. R. Roelfsema · 2000
Earlier work this paper cites.
Brain states: top-down influences in sensory processing
C. D. Gilbert and M. Sigman · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Top-down modulation of visual feature processing: the role of the inferior frontal junction
T. P. Zanto, M. T. Rubens, J. Bollinger, and A. Gazzaley · 2010
Earlier work this paper cites.
Deconvolutional networks
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus · 2010
Earlier work this paper cites.
Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory
T. P. Zanto, M. T. Rubens, A. Thangavel, and A. Gazzaley · 2011
Earlier work this paper cites.
Top-down modulation: bridging selective attention and working memory
A. Gazzaley and A. C. Nobre · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Learning hierarchical features for scene labeling
C. Farabet, C. Couprie, L. Najman, and Y. LeCun · 2013
Earlier work this paper cites.
The ventral visual pathway: an expanded neural framework for the processing of object quality
D. J. Kravitz, K. S. Saleem, C. I. Baker, L. G. Ungerleider, and M. Mishkin · 2013
Earlier work this paper cites.
Network model of top-down influences on local gain and contextual interactions in visual cortex
V. Piëch, W. Li, G. N. Reeke, and C. D. Gilbert · 2013
Earlier work this paper cites.
Pedestrian detection with unsupervised multi-stage feature learning
P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun · 2013
Earlier work this paper cites.
Deep neural networks for object detection
C. Szegedy, A. Toshev, and D. Erhan · 2013
Earlier work this paper cites.
Unrolling loopy top-down semantic feedback in convolutional deep networks
C. Gatta, A. Romero, and J. van de Veijer · 2014
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.
Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
Cited alongside, same era.
Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
Cited alongside, same era.
Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Cited alongside, same era.
Joint training of a convolutional network and a graphical model for human pose estimation
J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler · 2014
Cited alongside, same era.
Deeppose: Human pose estimation via deep neural networks
A. Toshev and C. Szegedy · 2014
Cited alongside, same era.
A hidden ambiguity of the term “feedback” in its use as an explanatory mechanism for psychophysical visual phenomena
T. Bachmann · 2015
Parsenet: Looking wider to see better
W. Liu, A. Rabinovich, and A. C. Berg · 2015
Later among the works it cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Later among the works it cites.
Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
Later among the works it cites.
Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollar · 2015
Later among the works it cites.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Later among the works it cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2015
Cited alongside, same era.
Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
S. Bell, C. L. Zitnick, K. Bala, and R. Girshick · 2015
Cited alongside, same era.
Human pose estimation with iterative error feedback
J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik · 2015
Cited alongside, same era.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
Cited alongside, same era.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Cited alongside, same era.
Flownet: Learning optical flow with convolutional networks
P. Fischer, A. Dosovitskiy, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox · 2015
Cited alongside, same era.
Later among the works it cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Later among the works it cites.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Later among the works it cites.
Designing deep networks for surface normal estimation
X. Wang, D. Fouhey, and A. Gupta · 2015
Later among the works it cites.
Holistically-nested edge detection
S. Xie and Z. Tu · 2015
Later among the works it cites.
Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2016
Closest in time.
Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2016
Closest in time.
Learning to refine object segments
P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár · 2016
Closest in time.
Optical flow estimation using a spatial pyramid network
A. Ranjan and M. J. Black · 2016
Closest in time.
Contextual priming and feedback for Faster R-CNN
A. Shrivastava and A. Gupta · 2016
Closest in time.
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Closest in time.
Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, and V. Vanhoucke · 2016
Closest in time.
Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al · 2017
Closest in time.