Fetching the paper…
Reading the bibliography…
Big data has had a great share in the success of deep learning in computer vision.
J. Li, X. Liang, Y. Wei, T. Xu, J. Feng, and S. Yan, “Perceptual generative adversarial networks for small object detection,” in Proc. IEEE CVPR , 2017, pp. 1951–1959
1959
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE CVPR , vol. 1, 2005, pp. 886–893
2005
Earlier work this paper cites.
S. Munder and D. M. Gavrila, “An experimental study on pedestrian classification,” IEEE TPAMI , vol. 28, no. 11, pp. 1863–1868, 2006
2006
Earlier work this paper cites.
J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” Journal of Machine Learning Research , pp. 1–30, 2006
2006
Earlier work this paper cites.
D. Gerónimo, A. Sappa, A. López, and D. Ponsa, “Adaptive image sampling and windows classification for on-board pedestrian detection,” in Proc. of the ICVS , vol. 39, 2007
2007
Earlier work this paper cites.
A. Ess, B. Leibe, and L. Van Gool, “Depth and appearance for mobile scene analysis,” in Proc. IEEE ICCV , 2007, pp. 1–8
2007
Earlier work this paper cites.
G. Overett, L. Petersson, N. Brewer, L. Andersson, and N. Pettersson, “A new pedestrian dataset for supervised learning,” in IEEE Intell. Veh , 2008, pp. 373–378
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in Proc. IEEE CVPR , 2009, pp. 248–255
2009
Earlier work this paper cites.
M. Enzweiler and D. M. Gavrila, “Monocular pedestrian detection: Survey and experiments,” IEEE TPAMI , vol. 31, no. 12, pp. 2179–2195, 2009
2009
Earlier work this paper cites.
C. Wojek, S. Walk, and B. Schiele, “Multi-cue onboard pedestrian detection,” in Proc. IEEE CVPR , 2009, pp. 794–801
2009
Earlier work this paper cites.
P. Dollár, Z. Tu, P. Perona, and S. Belongie, “Integral channel features,” in Proc. BMVC , 2009
2009
Earlier work this paper cites.
P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object Detection with Discriminatively Trained Part-based Models,” IEEE TPAMI , vol. 32, no. 9, pp. 1627–1645, 2010
2010
Earlier work this paper cites.
A. Torralba and A. A. Efros, “Unbiased look at dataset bias,” in Proc. IEEE CVPR , June 2011, pp. 1521–1528
2011
Earlier work this paper cites.
G. Sharma and F. Jurie, “Learning discriminative spatial representation for image classification,” in Proc. BMVC , 2011, pp. 1–11
2011
Earlier work this paper cites.
P. Dollár, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE TPAMI , vol. 34, no. 4, pp. 743–761, 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite,” in Proc. IEEE CVPR , 2012, pp. 3354–3361
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Adv. in NIPS , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Yan, X. Zhang, Z. Lei, S. Liao, and S. Z. Li, “Robust multi-resolution pedestrian detection in traffic scenes,” in Proc. IEEE CVPR , June 2013, pp. 3033–3040
2013
Earlier work this paper cites.
W. Ouyang and X. Wang, “Single-pedestrian detection aided by multi-pedestrian detection,” in Proc. IEEE CVPR , 2013, pp. 3198–3205
2013
Earlier work this paper cites.
R. Benenson, M. Mathias, T. Tuytelaars, and L. V. Gool, “Seeking the strongest rigid detector,” in Proc. IEEE CVPR , June 2013, pp. 3666–3673
2013
Earlier work this paper cites.
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, “Selective Search for Object Recognition,” IJCV , vol. 104, no. 2, pp. 154–171, 2013
2013
Earlier work this paper cites.
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” in Proc. IEEE CVPR , 2014, pp. 580–587
2014
Earlier work this paper cites.
R. Benenson, M. Omran, J. Hosang, and B. Schiele, “Ten years of pedestrian detection, what have we learned?” in Proc. of the ECCVWorkshop , 2014, pp. 613–627
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft COCO: Common objects in context,” in Proc. of the ECCV , 2014, pp. 740–755
2014
Earlier work this paper cites.
S. Zhang, C. Bauckhage, and A. B. Cremers, “Informed Haar-like features improve pedestrian detection,” in Proc. IEEE CVPR , June 2014, pp. 947–954
2014
Earlier work this paper cites.
P. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik, “Multiscale Combinatorial Grouping,” in Proc. IEEE CVPR , 2014, pp. 328–335
2014
Cited alongside, same era.
M.-M. Cheng, Z. Zhang, W.-Y. Lin, and P. H. S. Torr, “BING: Binarized Normed Gradients for Objectness Estimation at 300 fps,” in Proc. IEEE CVPR , 2014, pp. 3286–3293
2014
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Adv. in NIPS , 2014, pp. 2672–2680
2014
Cited alongside, same era.
P. Dollár, R. Appel, S. Belongie, and P. Perona, “Fast feature pyramids for object detection,” IEEE TPAMI , vol. 36, no. 8, pp. 1532–1545, Aug 2014
2014
Cited alongside, same era.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proc. of the ACM international conference on Multimedia . ACM, 2014, pp. 675–678
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. IEEE CVPR , 2016, pp. 779–788
2016
Later among the works it cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Proc. of the ECCV , 2016, pp. 21–37
2016
Later among the works it cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE CVPR , 2016, pp. 770–778
2016
Later among the works it cites.
2016
Later among the works it cites.
J. Hosang, R. Benenson, P. Dollár, and B. Schiele, “What Makes for Effective Detection Proposals?” IEEE TPAMI , vol. 38, no. 4, pp. 814–830, 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Adv. in NIPS , 2014, pp. 3320–3328
2014
Cited alongside, same era.
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “CNN features off-the-shelf: an astounding baseline for recognition,” in Proc. IEEE CVPR , 2014, pp. 512–519
2014
Cited alongside, same era.
R. B. Girshick, “Fast R-CNN,” in Proc. IEEE ICCV , 2015, pp. 1440–1448
2015
Cited alongside, same era.
S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in Adv. in NIPS , 2015, pp. 91–99
2015
Cited alongside, same era.
J. Hosang, M. Omran, R. Benenson, and B. Schiele, “Taking a Deeper Look at Pedestrians,” in Proc. IEEE CVPR , 2015, pp. 4073–4082
2015
Cited alongside, same era.
R. N. Rajaram, E. Ohn-Bar, and M. M. Trivedi, “An exploration of why and when pedestrian detection fails,” in Proc. of the IEEE ITSC , 2015, pp. 2335–2340
2015
Cited alongside, same era.
S. Hwang, J. Park, N. Kim, Y. Choi, and I. Kweon, “Multispectral pedestrian detection: Benchmark dataset and baseline,” in Proc. IEEE CVPR , 2015, pp. 1037–1045
2015
Cited alongside, same era.
2016
Later among the works it cites.
M. Braun, Q. Rao, Y. Wang, and F. Flohr, “Pose-RCNN: Joint object detection and pose estimation using 3D object proposals,” in Proc. of the IEEE ITSC , 2016, pp. 1546–1551
2016
Later among the works it cites.
Z. Cai, Q. Fan, R. Feris, and N. Vasconcelos, “A unified multi-scale deep convolutional neural network for fast object detection,” in Proc. of the ECCV , 2016
2016
Later among the works it cites.
F. Yang, W. Choi, and Y. Lin, “Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers,” in Proc. IEEE CVPR , 2016
2016
Later among the works it cites.
Y. Zhu, J. Wang, C. Zhao, H. Guo, and H. Lu, “Scale-adaptive deconvolutional regression network for pedestrian detection,” in Proc. ACCV , 2016
2016
Later among the works it cites.
A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in Proc. IEEE CVPR , 2016
2016
Later among the works it cites.
S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele, “How far are we from solving pedestrian detection?” Proc. IEEE CVPR , pp. 1259–1267, 2016
2016
Later among the works it cites.
L. Zhang, L. Lin, X. Liang, and K. He, “Is Faster R-CNN doing well for pedestrian detection?” in Proc. of the ECCV , 2016, pp. 443–457
2016
Later among the works it cites.
J. Redmon, “Darknet: Open source neural networks in c.” http://pjreddie.com/darknet/ , 2013-–2016
2016
Later among the works it cites.
S. Zhang, R. Benenson, and B. Schiele, “CityPersons: A Diverse Dataset for Pedestrian Detection,” Proc. IEEE CVPR , 2017
2017
Later among the works it cites.
C. Sun, A. Shrivastava, S. Singh, and A. Gupta, “Revisiting unreasonable effectiveness of data in deep learning era,” Proc. IEEE ICCV , 2017
2017
Later among the works it cites.
J. Redmon and A. Farhadi, “Yolo9000: Better, faster, stronger,” in Proc. IEEE CVPR , 2017, pp. 6517–6525
2017
Later among the works it cites.
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y.-W. Tai, and L. Xu, “Accurate single stage detector using recurrent rolling convolution,” in Proc. IEEE CVPR , 2017
2017
Later among the works it cites.
S. Huang and D. Ramanan, “Expecting the unexpected: Training detectors for unusual pedestrians with adversarial imposters,” in Proc. IEEE CVPR , 2017, pp. 4664–4673
2017
Later among the works it cites.
Y. Xiang, W. Choi, Y. Lin, and S. Savarese, “Subcategory-aware convolutional neural networks for object proposals and detection,” in Proc. IEEE WACV , 2017, pp. 924–933
2017
Later among the works it cites.
J. Mao, T. Xiao, Y. Jiang, and Z. Cao, “What can help pedestrian detection?” in Proc. IEEE CVPR , 2017, pp. 6034–6043
2017
Later among the works it cites.
N. Bodla, B. Singh, R. Chellappa, and L. S. Davis, “Soft-NMS – improving object detection with one line of code,” in Proc. IEEE ICCV , 2017
2017
Later among the works it cites.
J. Hosang, R. Benenson, and B. Schiele, “Learning non-maximum suppression,” in Proc. IEEE CVPR , 2017
2017
Later among the works it cites.
S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele, “Towards reaching human performance in pedestrian detection,” IEEE TPAMI , vol. 34, no. 4, pp. 973–985, 2018
2018
Closest in time.
“Berkeley deep drive dataset,” http://bdd-data.berkeley.edu/ , 2018
2018
Closest in time.
2018
Closest in time.
J. Li, X. Liang, S. Shen, T. Xu, and S. Yan, “Scale-aware fast R-CNN for pedestrian detection,” IEEE Transactions on Multimedia , vol. 20, no. 4, pp. 985–996, April 2018
2018
Closest in time.