Fetching the paper…
Reading the bibliography…
While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving.
Beyond sliding windows: Object localization by efficient subwindow search
C. H. Lampert, M. B. Blaschko, and T. Hofmann · 2008
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.
Deconvolutional networks
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus · 2010
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 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.
Scalable object detection using deep neural networks
D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov · 2013
Earlier work this paper cites.
A new performance measure and evaluation benchmark for road detection algorithms
J. Fritsch, T. Kuehnl, and A. Geiger · 2013
Earlier work this paper cites.
Kitti road public benchmark, 2013
A. Geiger · 2013
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik · 2013
Earlier work this paper cites.
Fast image scanning with deep max-pooling convolutional neural networks
A. Giusti, D. C. Ciresan, J. Masci, L. M. Gambardella, and J. Schmidhuber · 2013
Earlier work this paper cites.
Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2013
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2014
Earlier work this paper cites.
Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
Earlier work this paper cites.
How good are detection proposals, really?
J. H. Hosang, R. Benenson, and B. Schiele · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
H. Li, R. Zhao, and X. Wang · 2014
Earlier work this paper cites.
Deep deconvolutional networks for scene parsing, 2014
R. Mohan · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Cited alongside, same era.
Facial landmark detection by deep multi-task learning
Z. Zhang, P. Luo, C. C. Loy, and X. Tang · 2014
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.
3d object proposals for accurate object class detection
X. Chen, K. Kundu, Y. Zhu, A. G. Berneshawi, H. Ma, S. Fidler, and R. Urtasun · 2015
Cited alongside, same era.
Object detection via a multi-region and semantic segmentation-aware cnn model
S. Gidaris and N. Komodakis · 2015
Cited alongside, same era.
Rotating your face using multi-task deep neural network
J. Yim, H. Jung, B. Yoo, C. Choi, D. Park, and J. Kim · 2015
Later among the works it cites.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2015
Later among the works it cites.
Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. S. Torr · 2015
Later among the works it cites.
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
Closest in time.
Monocular 3d object detection for autonomous driving
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun · 2016
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. B. Girshick · 2015
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
What makes for effective detection proposals?
J. H. Hosang, R. Benenson, P. Dollár, and B. Schiele · 2015
Cited alongside, same era.
SSD: single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, and S. E. Reed · 2015
Cited alongside, same era.
Representation learning using multi-task deep neural networks for semantic classification and information retrieval
X. Liu, J. Gao, X. He, L. Deng, K. Duh, and Y.-Y. Wang · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
Closest in time.
A guide to convolution arithmetic for deep learning
V. Dumoulin and F. Visin · 2016
Closest in time.
Generating images with recurrent adversarial networks
D. J. Im, C. D. Kim, H. Jiang, and R. Memisevic · 2016
Closest in time.
Map-supervised road detection
A. Laddha, M. K. Kocamaz, L. E. Navarro-Serment, and M. Hebert · 2016
Closest in time.
Find your way by observing the sun and other semantic cues
W.-C. Ma, S. Wang, M. A. Brubaker, S. Fidler, and R. Urtasun · 2016
Closest in time.
Efficient deep methods for monocular road segmentation
G. Oliveira, W. Burgard, and T. Brox · 2016
Closest in time.
Learning to refine object segments
P. O. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár · 2016
Closest in time.
R. Ranjan, V. M. Patel, and R. Chellappa · 2016
Closest in time.
Towards road type classification with occupancy grids
C. Seeger, A. Müller, L. Schwarz, and M. Manz · 2016
Closest in time.
End-to-end people detection in crowded scenes
R. Stewart, M. Andriluka, and A. Y. Ng · 2016
Closest in time.
Wider or deeper: Revisiting the resnet model for visual recognition
Z. Wu, C. Shen, and A. van den Hengel · 2016
Closest in time.
Fast lidar-based road detection using convolutional neural networks
L. Caltagirone, S. Scheidegger, L. Svensson, and M. Wahde · 2017
Closest in time.
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick · 2017
Closest in time.
Deep Fully Convolutional Networks with Random Data Augmentation for Enhanced Generalization in Road Detection
J. Muñoz-Bulnes, C. Fernandez, I. Parra, D. Fernández-Llorca, and M. A. Sotelo · 2017
Closest in time.