Fetching the paper…
Reading the bibliography…
Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features.
The representation and matching of pictorial structures
M. A. Fischler and R. A. Elschlager · 1973
Earlier work this paper cites.
Neural network model for a mechanism of pattern recognition unaffected by shift in position- neocognitron
K. Fukushima · 1979
Earlier work this paper cites.
Cresceptron: a self-organizing neural network which grows adaptively
J. Weng, N. Ahuja, and T. S. Huang · 1992
Earlier work this paper cites.
Learning algorithms for classification: A comparison on handwritten digit recognition
Y. LeCun, L. Jackel, L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, U. Muller, E. Sackinger, P. Simard, et al · 1995
Earlier work this paper cites.
Object detection with discriminatively trained part-based models
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan · 2010
Earlier work this paper cites.
Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, A. Senior, P. Tucker, K. Yang, Q. V. Le, et al · 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.
Prime object proposals with randomized prim’s algorithm
S. Manen, M. Guillaumin, and L. V. Gool · 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.
Deep neural networks for object detection
C. Szegedy, A. Toshev, and D. Erhan · 2013
Earlier work this paper cites.
Selective search for object recognition
J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W. Smeulders · 2013
Earlier work this paper cites.
Bing: Binarized normed gradients for objectness estimation at 300fps
M.-M. Cheng, Z. Zhang, W.-Y. Lin, and P. Torr · 2014
Cited alongside, same era.
Scalable object detection using deep neural networks
D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov · 2014
Cited alongside, same era.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, 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.
How good are detection proposals, really?
J. Hosang, R. Benenson, and B. Schiele · 2014
Cited alongside, same era.
Microsoft coco: Common objects in context
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Closest in time.
Edge boxes: Locating object proposals from edges
C. L. Zitnick and P. Dollár · 2014
Closest in time.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Closest in time.
Fast r-cnn
R. Girshick · 2015
Closest in time.
Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollar · 2015
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Cited alongside, same era.
Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection
W. Ouyang, P. Luo, X. Zeng, S. Qiu, Y. Tian, H. Li, S. Yang, Z. Wang, Y. Xiong, C. Qian, et al · 2014
Cited alongside, same era.
Training Deep Neural Networks on Noisy Labels with Bootstrapping
S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2014
Cited alongside, same era.
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, et al · 2014
Cited alongside, same era.
30hz object detection with dpm v5
M. A. Sadeghi and D. Forsyth · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Cited alongside, same era.
Multiscale combinatorial grouping for image segmentation and object proposal generation
J. Pont-Tuset, P. Arbeláez, J. Barron, F. Marques, and J. Malik · 2015
Closest in time.
You only look once: Unified, real-time object detection
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi · 2015
Closest in time.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Closest in time.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Closest in time.
Rethinking the incption architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, S. Jonathon, and Z. Wojna · 2015
Closest in time.