Fetching the paper…
Reading the bibliography…
Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years.
W. Pitts and W. S. McCulloch, “How we know universals the perception of auditory and visual forms,” The Bulletin of Mathematical Biophysics , vol. 9, no. 3, pp. 127–147, 1947
1947
Earlier work this paper cites.
F. M. Wadley, “Probit analysis: a statistical treatment of the sigmoid response curve,” Annals of the Entomological Soc. of America , vol. 67, no. 4, pp. 549–553, 1947
1947
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representation by back-propagation of errors,” Nature , vol. 323, no. 323, pp. 533–536, 1986
1986
Earlier work this paper cites.
C. Cortes and V. Vapnik, “Support vector machine,” Machine Learning , vol. 20, no. 3, pp. 273–297, 1995
1995
Earlier work this paper cites.
H. Kobatake and Y. Yoshinaga, “Detection of spicules on mammogram based on skeleton analysis.” IEEE Trans. Med. Imag. , vol. 15, no. 3, pp. 235–245, 1996
1996
Earlier work this paper cites.
Y. Freund and R. E. Schapire, “A desicion-theoretic generalization of on-line learning and an application to boosting,” J. of Comput. & Sys. Sci. , vol. 13, no. 5, pp. 663–671, 1997
1997
Earlier work this paper cites.
M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Trans. Signal Process. , vol. 45, pp. 2673–2681, 1997
1997
Earlier work this paper cites.
K. K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 20, no. 1, pp. 39–51, 2002
2002
Earlier work this paper cites.
R. Lienhart and J. Maydt, “An extended set of haar-like features for rapid object detection,” in ICIP , 2002
2002
Earlier work this paper cites.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Comput. Vision , vol. 60, no. 2, pp. 91–110, 2004
2004
Earlier work this paper cites.
P. Viola and M. Jones, “Robust real-time face detection,” Int. J. of Comput. Vision , vol. 57, no. 2, pp. 137–154, 2004
2004
Earlier work this paper cites.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in CVPR , 2005
2005
Earlier work this paper cites.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Sci. , vol. 313, pp. 504–507, 2006
2006
Earlier work this paper cites.
S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in CVPR , 2006
2006
Earlier work this paper cites.
C. Rother, L. Bordeaux, Y. Hamadi, and A. Blake, “Autocollage,” ACM Trans. on Graphics , vol. 25, no. 3, pp. 847–852, 2006
2006
Earlier work this paper cites.
D. Gavrila and S. Munder, “Multi-cue pedestrian detection and tracking from a moving vehicle,” Int. J. of Comput. Vision , vol. 73, pp. 41–59, 2006
2006
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge 2007 (voc 2007) results (2007),” 2008
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 CVPR , 2009
2009
Earlier work this paper cites.
K. Kavukcuoglu, R. Fergus, Y. LeCun et al. , “Learning invariant features through topographic filter maps,” in CVPR , 2009
2009
Earlier work this paper cites.
P. L. Rosin, “A simple method for detecting salient regions,” Pattern Recognition , vol. 42, no. 11, pp. 2363–2371, 2009
2009
Earlier work this paper cites.
D. Gao, S. Han, and N. Vasconcelos, “Discriminant saliency, the detection of suspicious coincidences, and applications to visual recognition,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 31, pp. 989–1005, 2009
2009
Earlier work this paper cites.
K. Kang, H. Li, T. Xiao, W. Ouyang, J. Yan, X. Liu, and X. Wang, “Object detection in videos with tubelet proposal networks,” in CVPR , 2017
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 Trans. Pattern Anal. Mach. Intell. , vol. 32, no. 9, p. 1627, 2010
2010
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 Trans. Pattern Anal. Mach. Intell. , vol. 32, pp. 1627–1645, 2010
2010
Earlier work this paper cites.
L. Deng, M. L. Seltzer, D. Yu, A. Acero, A.-r. Mohamed, and G. Hinton, “Binary coding of speech spectrograms using a deep auto-encoder,” in INTERSPEECH , 2010
2010
Earlier work this paper cites.
G. Dahl, A.-r. Mohamed, G. E. Hinton et al. , “Phone recognition with the mean-covariance restricted boltzmann machine,” in NIPS , 2010
2010
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in ICML , 2010
2010
Earlier work this paper cites.
K. Kavukcuoglu, P. Sermanet, Y.-L. Boureau, K. Gregor, M. Mathieu, and Y. LeCun, “Learning convolutional feature hierarchies for visual recognition,” in NIPS , 2010
2010
Earlier work this paper cites.
M. D. Zeiler, D. Krishnan, G. W. Taylor, and R. Fergus, “Deconvolutional networks,” in CVPR , 2010
2010
Earlier work this paper cites.
F. Perronnin, J. Sánchez, and T. Mensink, “Improving the fisher kernel for large-scale image classification,” in ECCV , 2010
2010
Earlier work this paper cites.
V. Movahedi and J. H. Elder, “Design and perceptual validation of performance measures for salient object segmentation,” in CVPRW , 2010
2010
Earlier work this paper cites.
V. Jain and E. Learned-Miller, “Fddb: A benchmark for face detection in unconstrained settings,” Tech. Rep., 2010
2010
Earlier work this paper cites.
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning,” in ICML , 2011
2011
Earlier work this paper cites.
G. E. Hinton, A. Krizhevsky, and S. D. Wang, “Transforming auto-encoders,” in ICANN , 2011
2011
Earlier work this paper cites.
G. W. Taylor, I. Spiro, C. Bregler, and R. Fergus, “Learning invariance through imitation,” in CVPR , 2011
2011
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge 2012 (voc2012) results (2012),” in http://www.pascal-network.org/challenges/VOC/voc2011/workshop/index.html , 2011
2011
Earlier work this paper cites.
T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum, “Learning to detect a salient object,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 33, no. 2, pp. 353–367, 2011
2011
Earlier work this paper cites.
C. Wojek, P. Dollar, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 34, no. 4, p. 743, 2012
2012
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in NIPS , 2012
2012
Earlier work this paper cites.
A. Stuhlsatz, J. Lippel, and T. Zielke, “Feature extraction with deep neural networks by a generalized discriminant analysis.” IEEE Trans. Neural Netw. & Learning Syst. , vol. 23, no. 4, pp. 596–608, 2012
2012
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al. , “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Process. Mag. , vol. 29, no. 6, pp. 82–97, 2012
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
C. Jung and C. Kim, “A unified spectral-domain approach for saliency detection and its application to automatic object segmentation,” IEEE Trans. Image Process. , vol. 21, no. 3, pp. 1272–1283, 2012
2012
Earlier work this paper cites.
X. Gao, N. Wang, D. Tao, and X. Li, “Face sketch¨cphoto synthesis and retrieval using sparse representation,” IEEE Trans. Circuits Syst. Video Technol. , vol. 22, no. 8, pp. 1213–1226, 2012
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
X. Ren and D. Ramanan, “Histograms of sparse codes for object detection,” in CVPR , 2013
2013
Earlier work this paper cites.
J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders, “Selective search for object recognition,” Int. J. of Comput. Vision , vol. 104, no. 2, pp. 154–171, 2013
2013
Earlier work this paper cites.
P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun, “Pedestrian detection with unsupervised multi-stage feature learning,” in CVPR , 2013
2013
Earlier work this paper cites.
J. Xue, J. Li, and Y. Gong, “Restructuring of deep neural network acoustic models with singular value decomposition.” in Interspeech , 2013
2013
Earlier work this paper cites.
C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networks for object detection,” in NIPS , 2013
2013
Earlier work this paper cites.
X. Li, Y. Li, C. Shen, A. Dick, and A. Van Den Hengel, “Contextual hypergraph modeling for salient object detection,” in ICCV , 2013
2013
Earlier work this paper cites.
H. Jiang, J. Wang, Z. Yuan, Y. Wu, N. Zheng, and S. Li, “Salient object detection: A discriminative regional feature integration approach,” in CVPR , 2013
2013
Earlier work this paper cites.
Q. Yan, L. Xu, J. Shi, and J. Jia, “Hierarchical saliency detection,” in CVPR , 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. Li and Y. Zhang, “Learning surf cascade for fast and accurate object detection,” in CVPR , 2013
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” Int. J. of Robotics Res. , vol. 32, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
M. Mathias, R. Benenson, R. Timofte, and L. Van Gool, “Handling occlusions with franken-classifiers,” in ICCV , 2013
2013
Earlier work this paper cites.
Y. Gao, M. Wang, Z.-J. Zha, J. Shen, X. Li, and X. Wu, “Visual-textual joint relevance learning for tag-based social image search,” IEEE Trans. Image Process. , vol. 22, no. 1, pp. 363–376, 2013
2013
Earlier work this paper cites.
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 ACM MM , 2014
2014
Earlier work this paper cites.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in CVPR , 2014
2014
Earlier work this paper cites.
E. Vig, M. Dorr, and D. Cox, “Large-scale optimization of hierarchical features for saliency prediction in natural images,” in CVPR , 2014
2014
Earlier work this paper cites.
D. Chen, S. Ren, Y. Wei, X. Cao, and J. Sun, “Joint cascade face detection and alignment,” in ECCV , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Oquab, L. Bottou, I. Laptev, J. Sivic et al. , “Weakly supervised object recognition with convolutional neural networks,” in NIPS , 2014
2014
Earlier work this paper cites.
M. Oquab, L. Bottou, I. Laptev, and J. Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in CVPR , 2014
2014
Earlier work this paper cites.
Z.-Q. Zhao, B.-J. Xie, Y.-m. Cheung, and X. Wu, “Plant leaf identification via a growing convolution neural network with progressive sample learning,” in ACCV , 2014
2014
Earlier work this paper cites.
A. Babenko, A. Slesarev, A. Chigorin, and V. Lempitsky, “Neural codes for image retrieval,” in ECCV , 2014
2014
Earlier work this paper cites.
J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li, “Deep learning for content-based image retrieval: A comprehensive study,” in ACM MM , 2014
2014
Earlier work this paper cites.
D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov, “Scalable object detection using deep neural networks,” in CVPR , 2014
2014
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Geodesic object proposals,” in ECCV , 2014
2014
Earlier work this paper cites.
P. Arbeláez, J. Pont-Tuset, J. T. Barron, F. Marques, and J. Malik, “Multiscale combinatorial grouping,” in CVPR , 2014
2014
Earlier work this paper cites.
C. L. Zitnick and P. Dollár, “Edge boxes: Locating object proposals from edges,” in ECCV , 2014
2014
Earlier work this paper cites.
S. Gupta, R. Girshick, P. Arbeláez, and J. Malik, “Learning rich features from rgb-d images for object detection and segmentation,” in ECCV , 2014
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 ECCV , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in ECCV , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. Li, X. Hou, C. Koch, J. M. Rehg, and A. L. Yuille, “The secrets of salient object segmentation,” in CVPR , 2014
2014
Cited alongside, same era.
N. Wang, D. Tao, X. Gao, X. Li, and J. Li, “A comprehensive survey to face hallucination,” Int. J. of Comput. Vision , vol. 106, no. 1, pp. 9–30, 2014
2014
Cited alongside, same era.
B. Yang, J. Yan, Z. Lei, and S. Z. Li, “Aggregate channel features for multi-view face detection,” in IJCB , 2014
2014
Cited alongside, same era.
M. Mathias, R. Benenson, M. Pedersoli, and L. Van Gool, “Face detection without bells and whistles,” in ECCV , 2014
2014
Cited alongside, same era.
P. Dollár, R. Appel, S. Belongie, and P. Perona, “Fast feature pyramids for object detection,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 36, no. 8, pp. 1532–1545, 2014
2014
Cited alongside, same era.
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 CVPR , 2016
2016
Later among the works it cites.
W. Ouyang, X. Wang, C. Zhang, and X. Yang, “Factors in finetuning deep model for object detection with long-tail distribution,” in CVPR , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
W. Shang, K. Sohn, D. Almeida, and H. Lee, “Understanding and improving convolutional neural networks via concatenated rectified linear units,” in ICML , 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Tang, M. Andriluka, and B. Schiele, “Detection and tracking of occluded people,” Int. J. of Comput. Vision , vol. 110, pp. 58–69, 2014
2014
Cited alongside, same era.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial nets,” in NIPS , 2014
2014
Cited alongside, same era.
C. Wang, W. Ren, K. Huang, and T. Tan, “Weakly supervised object localization with latent category learning,” in ECCV , 2014
2014
Cited alongside, same era.
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “Fitnets: Hints for thin deep nets,” Comput. Sci. , 2014
2014
Cited alongside, same era.
C. Chen, A. Seff, A. L. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in ICCV , 2015
2015
Cited alongside, same era.
R. Girshick, “Fast r-cnn,” in ICCV , 2015
2015
Cited alongside, same era.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in NIPS , 2015, pp. 91–99
2015
Cited alongside, same era.
2016
Later among the works it cites.
W.-C. Tu, S. He, Q. Yang, and S.-Y. Chien, “Real-time salient object detection with a minimum spanning tree,” in CVPR , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
2016
Later among the works it cites.
X. Li, L. Zhao, L. Wei, M.-H. Yang, F. Wu, Y. Zhuang, H. Ling, and J. Wang, “Deepsaliency: Multi-task deep neural network model for salient object detection,” IEEE Trans. Image Process. , vol. 25, no. 8, pp. 3919–3930, 2016
2016
Later among the works it cites.
Y. Tang and X. Wu, “Saliency detection via combining region-level and pixel-level predictions with cnns,” in ECCV , 2016
2016
Later among the works it cites.
G. Li and Y. Yu, “Deep contrast learning for salient object detection,” in CVPR , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
M. Cornia, L. Baraldi, G. Serra, and R. Cucchiara, “A deep multi-level network for saliency prediction,” in ICPR , 2016
2016
Later among the works it cites.
G. Li and Y. Yu, “Visual saliency detection based on multiscale deep cnn features,” IEEE Trans. Image Process. , vol. 25, no. 11, pp. 5012–5024, 2016
2016
Later among the works it cites.
J. Pan, E. Sayrol, X. Giro-i Nieto, K. McGuinness, and N. E. O’Connor, “Shallow and deep convolutional networks for saliency prediction,” in CVPR , 2016
2016
Later among the works it cites.
J. Kuen, Z. Wang, and G. Wang, “Recurrent attentional networks for saliency detection,” in CVPR , 2016
2016
Later among the works it cites.
Y. Tang, X. Wu, and W. Bu, “Deeply-supervised recurrent convolutional neural network for saliency detection,” in ACM MM , 2016
2016
Later among the works it cites.
G. Lee, Y.-W. Tai, and J. Kim, “Deep saliency with encoded low level distance map and high level features,” in CVPR , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
C. Peng, N. Wang, X. Gao, and J. Li, “Face recognition from multiple stylistic sketches: Scenarios, datasets, and evaluation,” in ECCV , 2016
2016
Later among the works it cites.
C. Peng, X. Gao, N. Wang, D. Tao, X. Li, and J. Li, “Multiple representations-based face sketch-photo synthesis.” IEEE Trans. Neural Netw. & Learning Syst. , vol. 27, no. 11, pp. 2201–2215, 2016
2016
Later among the works it cites.
J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang, “Unitbox: An advanced object detection network,” in ACM MM , 2016
2016
Later among the works it cites.
Y. Li, B. Sun, T. Wu, and Y. Wang, “face detection with end-to-end integration of a convnet and a 3d model,” in ECCV , 2016
2016
Later among the works it cites.
K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Process. Lett. , vol. 23, no. 10, pp. 1499–1503, 2016
2016
Later among the works it cites.
I. A. Kalinovsky and V. G. Spitsyn, “Compact convolutional neural network cascadefor face detection,” in CEUR Workshop , 2016
2016
Later among the works it cites.
H. Qin, J. Yan, X. Li, and X. Hu, “Joint training of cascaded cnn for face detection,” in CVPR , 2016
2016
Later among the works it cites.
S. Liao, A. K. Jain, and S. Z. Li, “A fast and accurate unconstrained face detector,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 2, pp. 211–223, 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
S. Paisitkriangkrai, C. Shen, and A. van den Hengel, “Pedestrian detection with spatially pooled features and structured ensemble learning,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, pp. 1243–1257, 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 ECCV , 2016
2016
Later among the works it cites.
2016
Later among the works it cites.
D. Tomé, L. Bondi, L. Baroffio, S. Tubaro, E. Plebani, and D. Pau, “Reduced memory region based deep convolutional neural network detection,” in ICCE-Berlin , 2016
2016
Later among the works it cites.
B. Yang, J. Yan, Z. Lei, and S. Z. Li, “Craft objects from images,” in CVPR , 2016
2016
Later among the works it cites.
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime multi-person 2d pose estimation using part affinity fields,” in CVPR , 2017
2017
Later among the works it cites.
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia, “Multi-view 3d object detection network for autonomous driving,” in CVPR , 2017
2017
Later among the works it cites.
A. Dundar, J. Jin, B. Martini, and E. Culurciello, “Embedded streaming deep neural networks accelerator with applications,” IEEE Trans. Neural Netw. & Learning Syst. , vol. 28, no. 7, pp. 1572–1583, 2017
2017
Later among the works it cites.
S. H. Khan, M. Hayat, M. Bennamoun, F. A. Sohel, and R. Togneri, “Cost-sensitive learning of deep feature representations from imbalanced data.” IEEE Trans. Neural Netw. & Learning Syst. , vol. PP, no. 99, pp. 1–15, 2017
2017
Later among the works it cites.
H. Jiang and E. Learned-Miller, “Face detection with the faster r-cnn,” in FG , 2017
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 WACV , 2017
2017
Later among the works it cites.
Z.-Q. Zhao, H. Bian, D. Hu, W. Cheng, and H. Glotin, “Pedestrian detection based on fast r-cnn and batch normalization,” in ICIC , 2017
2017
Later among the works it cites.
T.-Y. Lin, P. Dollár, R. B. Girshick, K. He, B. Hariharan, and S. J. Belongie, “Feature pyramid networks for object detection,” in CVPR , 2017
2017
Later among the works it cites.
K. He, G. Gkioxari, P. Dollár, and R. B. Girshick, “Mask r-cnn,” in ICCV , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
Z. Shen, Z. Liu, J. Li, Y. G. Jiang, Y. Chen, and X. Xue, “Dsod: Learning deeply supervised object detectors from scratch,” in ICCV , 2017
2017
Later among the works it cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 6, pp. 1137–1149, 2017
2017
Later among the works it cites.
A. Arnab and P. H. S. Torr, “Pixelwise instance segmentation with a dynamically instantiated network,” in CVPR , 2017
2017
Later among the works it cites.
Y. Li, H. Qi, J. Dai, X. Ji, and Y. Wei, “Fully convolutional instance-aware semantic segmentation,” in CVPR , 2017
2017
Later among the works it cites.
S. Brahmbhatt, H. I. Christensen, and J. Hays, “Stuffnet: Using ¡®stuff¡¯to improve object detection,” in WACV , 2017
2017
Later among the works it cites.
S. Ren, K. He, R. Girshick, X. Zhang, and J. Sun, “Object detection networks on convolutional feature maps,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 7, pp. 1476–1481, 2017
2017
Later among the works it cites.
S. Xie, R. B. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in CVPR , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
J. Yang and M.-H. Yang, “Top-down visual saliency via joint crf and dictionary learning,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 3, pp. 576–588, 2017
2017
Later among the works it cites.
Z. Luo, A. Mishra, A. Achkar, J. Eichel, S. Li, and P.-M. Jodoin, “Non-local deep features for salient object detection,” in CVPR , 2017
2017
Later among the works it cites.
S. Yang, Y. Xiong, C. C. Loy, and X. Tang, “Face detection through scale-friendly deep convolutional networks,” in CVPR , 2017
2017
Later among the works it cites.
Z. Hao, Y. Liu, H. Qin, J. Yan, X. Li, and X. Hu, “Scale-aware face detection,” in CVPR , 2017
2017
Later among the works it cites.
H. Wang, Z. Li, X. Ji, and Y. Wang, “Face r-cnn,” arXiv:1706.01061 , 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
P. Hu and D. Ramanan, “Finding tiny faces,” in CVPR , 2017
2017
Later among the works it cites.
S. Xu, Y. Cheng, K. Gu, Y. Yang, S. Chang, and P. Zhou, “Jointly attentive spatial-temporal pooling networks for video-based person re-identification,” in ICCV , 2017
2017
Later among the works it cites.
Z. Liu, D. Wang, and H. Lu, “Stepwise metric promotion for unsupervised video person re-identification,” in ICCV , 2017
2017
Later among the works it cites.
X. Du, M. El-Khamy, J. Lee, and L. Davis, “Fused dnn: A deep neural network fusion approach to fast and robust pedestrian detection,” in WACV , 2017
2017
Later among the works it cites.
Q. Hu, P. Wang, C. Shen, A. van den Hengel, and F. Porikli, “Pushing the limits of deep cnns for pedestrian detection,” IEEE Trans. Circuits Syst. Video Technol. , 2017
2017
Later among the works it cites.
T. Kong, F. Sun, A. Yao, H. Liu, M. Lv, and Y. Chen, “Ron: Reverse connection with objectness prior networks for object detection,” in CVPR , 2017
2017
Later among the works it cites.
Y. Fang, K. Kuan, J. Lin, C. Tan, and V. Chandrasekhar, “Object detection meets knowledge graphs,” in IJCAI , 2017
2017
Later among the works it cites.
S. Welleck, J. Mao, K. Cho, and Z. Zhang, “Saliency-based sequential image attention with multiset prediction,” in NIPS , 2017
2017
Later among the works it cites.
S. Azadi, J. Feng, and T. Darrell, “Learning detection with diverse proposals,” in CVPR , 2017
2017
Later among the works it cites.
P. Dabkowski and Y. Gal, “Real time image saliency for black box classifiers,” in NIPS , 2017
2017
Later among the works it cites.
I. Croitoru, S.-V. Bogolin, and M. Leordeanu, “Unsupervised learning from video to detect foreground objects in single images,” in ICCV , 2017
2017
Later among the works it cites.
D. P. Papadopoulos, J. R. R. Uijlings, F. Keller, and V. Ferrari, “Training object class detectors with click supervision,” in CVPR , 2017
2017
Later among the works it cites.
J. Huang, V. Rathod, C. Sun, M. Zhu, A. K. Balan, A. Fathi, I. Fischer, Z. Wojna, Y. S. Song, S. Guadarrama, and K. Murphy, “Speed/accuracy trade-offs for modern convolutional object detectors,” in CVPR , 2017
2017
Later among the works it cites.
Q. Li, S. Jin, and J. Yan, “Mimicking very efficient network for object detection,” in CVPR , 2017
2017
Later among the works it cites.
J. Dong, X. Fei, and S. Soatto, “Visual-inertial-semantic scene representation for 3d object detection,” in CVPR , 2017
2017
Later among the works it cites.
R. J. Cintra, S. Duffner, C. Garcia, and A. Leite, “Low-complexity approximate convolutional neural networks,” IEEE Trans. Neural Netw. & Learning Syst. , vol. PP, no. 99, pp. 1–12, 2018
2018
Closest in time.
A. Majumder, L. Behera, and V. K. Subramanian, “Automatic facial expression recognition system using deep network-based data fusion,” IEEE Trans. Cybern. , vol. 48, pp. 103–114, 2018
2018
Closest in time.
Z. Jiang and D. Q. Huynh, “Multiple pedestrian tracking from monocular videos in an interacting multiple model framework,” IEEE Trans. Image Process. , vol. 27, pp. 1361–1375, 2018
2018
Closest in time.
A. Khan, B. Rinner, and A. Cavallaro, “Cooperative robots to observe moving targets: Review,” IEEE Trans. Cybern. , vol. 48, pp. 187–198, 2018
2018
Closest in time.