Fetching the paper…
Reading the bibliography…
Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in international Conference on computer vision & Pattern Recognition (CVPR’05)
2005
Earlier work this paper cites.
A. Ess, B. Leibe, and L. Van Gool, “Depth and appearance for mobile scene analysis,” in 2007 IEEE 11th International Conference on Computer Vision
2007
Earlier work this paper cites.
P. Müller, G. Zeng, P. Wonka, and L. Van Gool, “Image-based procedural modeling of facades,” in ACM Transactions on Graphics (TOG)
2007
Earlier work this paper cites.
M. Enzweiler and D. M. Gavrila, “Monocular pedestrian detection: Survey and experiments,” IEEE transactions on pattern analysis and machine intelligence
2008
Earlier work this paper cites.
E. Wu, W. Liu, and S. Chawla, “Spatio-temporal outlier detection in precipitation data,” in International Workshop on Knowledge Discovery from Sensor Data
2008
Earlier work this paper cites.
G. Schindler, P. Krishnamurthy, R. Lublinerman, Y. Liu, and F. Dellaert, “Detecting and matching repeated patterns for automatic geo-tagging in urban environments,” in 2008 IEEE Conference on Computer Vision and Pattern Recognition
2008
Earlier work this paper cites.
C. Wojek, S. Walk, and B. Schiele, “Multi-cue onboard pedestrian detection,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition
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 transactions on pattern analysis and machine intelligence
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” International Journal of Computer Vision
2010
Earlier work this paper cites.
P. Zhao, T. Fang, J. Xiao, H. Zhang, Q. Zhao, and L. Quan, “Rectilinear parsing of architecture in urban environment,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
2010
Earlier work this paper cites.
C. Wu, J.-M. Frahm, and M. Pollefeys, “Detecting large repetitive structures with salient boundaries,” in European conference on computer vision
2010
Earlier work this paper cites.
O. Barinova, V. Lempitsky, E. Tretiak, and P. Kohli, “Geometric image parsing in man-made environments,” in European conference on computer vision
2010
Earlier work this paper cites.
O. Teboul, I. Kokkinos, L. Simon, P. Koutsourakis, and N. Paragios, “Shape grammar parsing via reinforcement learning,” in CVPR 2011
2011
Earlier work this paper cites.
C.-H. Shen, S.-S. Huang, H. Fu, and S.-M. Hu, “Adaptive partitioning of urban facades,” in ACM Transactions on Graphics (TOG)
2011
Earlier work this paper cites.
S. Gandy, B. Recht, and I. Yamada, “Tensor completion and low-n-rank tensor recovery via convex optimization,” Inverse Problems
2011
Earlier work this paper cites.
E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?,” Journal of the ACM (JACM)
2011
Earlier work this paper cites.
A. Khosla, N. Jayadevaprakash, B. Yao, and F.-F. Li, “Novel dataset for fine-grained image categorization: Stanford dogs,” in Proc. CVPR Workshop on Fine-Grained Visual Categorization (FGVC)
2011
Earlier work this paper cites.
P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Transactions on Pattern Analysis and Machine Intelligence
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 2012 IEEE Conference on Computer Vision and Pattern Recognition
2012
Earlier work this paper cites.
M. J. Saberian and N. Vasconcelos, “Learning optimal embedded cascades,” IEEE transactions on pattern analysis and machine intelligence
2012
Earlier work this paper cites.
Y. Wang, H. Sundaram, and L. Xie, “Social event detection with interaction graph modeling,” in Proceedings of the 20th ACM international conference on Multimedia
2012
Earlier work this paper cites.
J. Liu, P. Musialski, P. Wonka, and J. Ye, “Tensor completion for estimating missing values in visual data,” IEEE transactions on pattern analysis and machine intelligence
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
S. Liu, M. Yamada, N. Collier, and M. Sugiyama, “Change-point detection in time-series data by relative density-ratio estimation,” Neural Networks
2013
Earlier work this paper cites.
S. Friedman and I. Stamos, “Online detection of repeated structures in point clouds of urban scenes for compression and registration,” International journal of computer vision
2013
Earlier work this paper cites.
F. Li, T. Kim, A. Humayun, D. Tsai, and J. M. Rehg, “Video segmentation by tracking many figure-ground segments,” in Proceedings of the IEEE International Conference on Computer Vision
2013
Earlier work this paper cites.
P. Ochs, J. Malik, and T. Brox, “Segmentation of moving objects by long term video analysis,” IEEE transactions on pattern analysis and machine intelligence
2013
Earlier work this paper cites.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in The IEEE International Conference on Computer Vision (ICCV) Workshops
2013
Earlier work this paper cites.
2013
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 Computer Vision – ECCV 2014
2014
Earlier work this paper cites.
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 2014 IEEE Conference on Computer Vision and Pattern Recognition
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
P. Dollár, R. Appel, S. Belongie, and P. Perona, “Fast feature pyramids for object detection,” IEEE transactions on pattern analysis and machine intelligence
2014
Earlier work this paper cites.
J. Han, D. Zhang, G. Cheng, L. Guo, and J. Ren, “Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning,” IEEE Transactions on Geoscience and Remote Sensing
2014
Earlier work this paper cites.
X. Chen, S. Xiang, C.-L. Liu, and C.-H. Pan, “Vehicle detection in satellite images by hybrid deep convolutional neural networks,” IEEE Geoscience and remote sensing letters
2014
Earlier work this paper cites.
G. Cheng, J. Han, P. Zhou, and L. Guo, “Multi-class geospatial object detection and geographic image classification based on collection of part detectors,” ISPRS Journal of Photogrammetry and Remote Sensing
2014
Earlier work this paper cites.
A. Cohen, A. G. Schwing, and M. Pollefeys, “Efficient structured parsing of facades using dynamic programming,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2014
Earlier work this paper cites.
Z. Liu, X. Zhang, S. Luo, and O. Le Meur, “Superpixel-based spatiotemporal saliency detection,” IEEE transactions on circuits and systems for video technology
2014
Earlier work this paper cites.
F. Zhou, S. Bing Kang, and M. F. Cohen, “Time-mapping using space-time saliency,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2014
Earlier work this paper cites.
M. Sun, A. Farhadi, and S. Seitz, “Ranking domain-specific highlights by analyzing edited videos,” in European conference on computer vision
2014
Earlier work this paper cites.
D. Potapov, M. Douze, Z. Harchaoui, and C. Schmid, “Category-specific video summarization,” in European conference on computer vision
2014
Earlier work this paper cites.
X. Chen and A. L. Yuille, “Articulated pose estimation by a graphical model with image dependent pairwise relations,” in Advances in neural information processing systems
2014
Earlier work this paper cites.
A. Toshev and C. Szegedy, “Deeppose: Human pose estimation via deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2014
Earlier work this paper cites.
W. Ouyang, X. Chu, and X. Wang, “Multi-source deep learning for human pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2014
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision
2015
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in 2015 IEEE International Conference on Computer Vision (ICCV)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. Yoo, S. Park, J.-Y. Lee, A. S. Paek, and I. So Kweon, “Attentionnet: Aggregating weak directions for accurate object detection,” in Proceedings of the IEEE International Conference on Computer Vision
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE transactions on pattern analysis and machine intelligence
2015
Earlier work this paper cites.
M. Jiang, A. Beutel, P. Cui, B. Hooi, S. Yang, and C. Faloutsos, “A general suspiciousness metric for dense blocks in multimodal data,” in 2015 IEEE International Conference on Data Mining
2015
Earlier work this paper cites.
K. Liu and G. Mattyus, “Fast multiclass vehicle detection on aerial images,” IEEE Geoscience and Remote Sensing Letters
2015
Earlier work this paper cites.
M. Schinas, S. Papadopoulos, G. Petkos, Y. Kompatsiaris, and P. A. Mitkas, “Multimodal graph-based event detection and summarization in social media streams,” in Proceedings of the 23rd ACM international conference on Multimedia
2015
Earlier work this paper cites.
M. Kozinski, R. Gadde, S. Zagoruyko, G. Obozinski, and R. Marlet, “A mrf shape prior for facade parsing with occlusions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2015
Earlier work this paper cites.
H. Kim, Y. Kim, J.-Y. Sim, and C.-S. Kim, “Spatiotemporal saliency detection for video sequences based on random walk with restart,” IEEE Transactions on Image Processing
2015
Earlier work this paper cites.
W. Wang, J. Shen, and L. Shao, “Consistent video saliency using local gradient flow optimization and global refinement,” IEEE Transactions on Image Processing
2015
Earlier work this paper cites.
W. Wang, J. Shen, and F. Porikli, “Saliency-aware geodesic video object segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2015
Earlier work this paper cites.
J. Zhang, S. Sclaroff, Z. Lin, X. Shen, B. Price, and R. Mech, “Minimum barrier salient object detection at 80 fps,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
H. Yang, B. Wang, S. Lin, D. Wipf, M. Guo, and B. Guo, “Unsupervised extraction of video highlights via robust recurrent auto-encoders,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
W. Liu, T. Mei, Y. Zhang, C. Che, and J. Luo, “Multi-task deep visual-semantic embedding for video thumbnail selection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
L. Wang, W. Ouyang, X. Wang, and H. Lu, “Visual tracking with fully convolutional networks,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
X. Fan, K. Zheng, Y. Lin, and S. Wang, “Combining local appearance and holistic view: Dual-source deep neural networks for human pose estimation,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2015
Earlier work this paper cites.
T.-Y. Lin, A. RoyChowdhury, and S. Maji, “Bilinear cnn models for fine-grained visual recognition,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Computer Vision – ECCV 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016
Earlier work this paper cites.
A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
S. Bell, C. Lawrence Zitnick, K. Bala, and R. Girshick, “Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
J. Dai, Y. Li, K. He, and J. Sun, “R-fcn: Object detection via region-based fully convolutional networks,” in Advances in neural information processing systems
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
X. Li, F. Flohr, Y. Yang, H. Xiong, M. Braun, S. Pan, K. Li, and D. M. Gavrila, “A new benchmark for vision-based cyclist detection,” in 2016 IEEE Intelligent Vehicles Symposium (IV)
2016
Earlier work this paper cites.
J. Yu, Y. Jiang, Z. Wang, Z. Cao, and T. Huang, “Unitbox: An advanced object detection network,” in Proceedings of the 24th ACM international conference on Multimedia
2016
Earlier work this paper cites.
M. Bucher, S. Herbin, and F. Jurie, “Hard negative mining for metric learning based zero-shot classification,” in European Conference on Computer Vision
2016
Earlier work this paper cites.
J. Hosang, R. Benenson, and B. Schiele, “A convnet for non-maximum suppression,” in German Conference on Pattern Recognition
2016
Earlier work this paper cites.
G. Cheng, P. Zhou, and J. Han, “Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing
2016
Earlier work this paper cites.
F. Zhang, B. Du, L. Zhang, and M. Xu, “Weakly supervised learning based on coupled convolutional neural networks for aircraft detection,” IEEE Transactions on Geoscience and Remote Sensing
2016
Earlier work this paper cites.
S. Wang, M. Wang, S. Yang, and L. Jiao, “New hierarchical saliency filtering for fast ship detection in high-resolution sar images,” IEEE Transactions on Geoscience and Remote Sensing
2016
Earlier work this paper cites.
S. Razakarivony and F. Jurie, “Vehicle detection in aerial imagery: A small target detection benchmark,” Journal of Visual Communication and Image Representation
2016
Earlier work this paper cites.
G. Cheng and J. Han, “A survey on object detection in optical remote sensing images,” ISPRS Journal of Photogrammetry and Remote Sensing
2016
Earlier work this paper cites.
H. Bilen and A. Vedaldi, “Weakly supervised deep detection networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
V. Kantorov, M. Oquab, M. Cho, and I. Laptev, “Contextlocnet: Context-aware deep network models for weakly supervised localization,” in European Conference on Computer Vision
2016
Earlier work this paper cites.
Y. Li, L. Liu, C. Shen, and A. van den Hengel, “Image co-localization by mimicking a good detector’s confidence score distribution,” in European Conference on Computer Vision
2016
Earlier work this paper cites.
Z. Liu, J. Li, L. Ye, G. Sun, and L. Shen, “Saliency detection for unconstrained videos using superpixel-level graph and spatiotemporal propagation,” IEEE transactions on circuits and systems for video technology
2016
Earlier work this paper cites.
W.-C. Tu, S. He, Q. Yang, and S.-Y. Chien, “Real-time salient object detection with a minimum spanning tree,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
T. Xi, W. Zhao, H. Wang, and W. Lin, “Salient object detection with spatiotemporal background priors for video,” IEEE Transactions on Image Processing
2016
Earlier work this paper cites.
T. Yao, T. Mei, and Y. Rui, “Highlight detection with pairwise deep ranking for first-person video summarization,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
X. Ren, Y. Zhou, J. He, K. Chen, X. Yang, and J. Sun, “A convolutional neural network-based chinese text detection algorithm via text structure modeling,” IEEE Transactions on Multimedia
2016
Earlier work this paper cites.
Z. Zhang, C. Zhang, W. Shen, C. Yao, W. Liu, and X. Bai, “Multi-oriented text detection with fully convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. Kang, W. Ouyang, H. Li, and X. Wang, “Object detection from video tubelets with convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3d object detection for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
A. Bulat and G. Tzimiropoulos, “Human pose estimation via convolutional part heatmap regression,” in European Conference on Computer Vision
2016
Earlier work this paper cites.
A. Newell, K. Yang, and J. Deng, “Stacked hourglass networks for human pose estimation,” in European Conference on Computer Vision
2016
Earlier work this paper cites.
S.-E. Wei, V. Ramakrishna, T. Kanade, and Y. Sheikh, “Convolutional pose machines,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in 2017 IEEE International Conference on Computer Vision (ICCV)
2017
Earlier work this paper cites.
T. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017
Earlier work this paper cites.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2017
Earlier work this paper cites.
W. Rawat and Z. Wang, “Deep convolutional neural networks for image classification: A comprehensive review,” Neural computation
2017
Cited alongside, same era.
J. Redmon and A. Farhadi, “Yolo9000: Better, faster, stronger,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2017
Cited alongside, same era.
T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in 2017 IEEE International Conference on Computer Vision (ICCV)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision
R. Laroca, E. Severo, L. A. Zanlorensi, L. S. Oliveira, G. R. Gonçalves, W. R. Schwartz, and D. Menotti, “A robust real-time automatic license plate recognition based on the yolo detector,” in 2018 International Joint Conference on Neural Networks (IJCNN)
2018
Later among the works it cites.
A. S. Nair, S. Raju, K. Harikrishnan, and A. Mathew, “A survey of techniques for license plate detection and recognition,” i-manager’s Journal on Image Processing
2018
Later among the works it cites.
2018
Later among the works it cites.
K. Banerjee, D. Notz, J. Windelen, S. Gavarraju, and M. He, “Online camera lidar fusion and object detection on hybrid data for autonomous driving,” in 2018 IEEE Intelligent Vehicles Symposium (IV)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
Y. Zhu, C. Zhao, J. Wang, X. Zhao, Y. Wu, and H. Lu, “Couplenet: Coupling global structure with local parts for object detection,” in Proceedings of the IEEE International Conference on Computer Vision
2017
Cited alongside, same era.
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al
2017
Cited alongside, same era.
N. Bodla, B. Singh, R. Chellappa, and L. S. Davis, “Soft-nms–improving object detection with one line of code,” in Proceedings of the IEEE International Conference on Computer Vision
2017
Cited alongside, same era.
T. Kong, F. Sun, A. Yao, H. Liu, M. Lu, and Y. Chen, “Ron: Reverse connection with objectness prior networks for object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2017
Cited alongside, same era.
S. Zhang, R. Benenson, and B. Schiele, “Citypersons: A diverse dataset for pedestrian detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Li and Z. Wang, “Real-time traffic sign recognition based on efficient cnns in the wild,” IEEE Transactions on Intelligent Transportation Systems
2018
Later among the works it cites.
US Patent 9,865,165
T. Moritani, Y. Otsubo, and T. Arinaga, “Traffic sign recognition system,” Jan. 9 2018 · 2018
Later among the works it cites.
S. Khalid, N. Muhammad, and M. Sharif, “Automatic measurement of the traffic sign with digital segmentation and recognition,” IET Intelligent Transport Systems
2018
Later among the works it cites.
Á. Arcos-García, J. A. Álvarez-García, and L. M. Soria-Morillo, “Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods,” Neural Networks
2018
Later among the works it cites.
D. Li, D. Zhao, Y. Chen, and Q. Zhang, “Deepsign: Deep learning based traffic sign recognition,” in 2018 international joint conference on neural networks (IJCNN)
2018
Later among the works it cites.
B.-X. Wu, P.-Y. Wang, Y.-T. Yang, and J.-I. Guo, “Traffic sign recognition with light convolutional networks,” in 2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)
2018
Later among the works it cites.
S. Zhou, W. Liang, J. Li, and J.-U. Kim, “Improved vgg model for road traffic sign recognition,” Computers, Materials and Continua
2018
Later among the works it cites.
N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra, H. Kittler, et al
2018
Later among the works it cites.
S. Naji, H. A. Jalab, and S. A. Kareem, “A survey on skin detection in colored images,” Artificial Intelligence Review
2018
Later among the works it cites.
M. Hasan, M. A. Orgun, and R. Schwitter, “A survey on real-time event detection from the twitter data stream,” Journal of Information Science
2018
Later among the works it cites.
J. Liu, E. Psarakis, Y. Feng, and I. Stamos, “A kronecker product model for repeated pattern detection on 2d urban images,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2018
Later among the works it cites.
P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang, “Bottom-up and top-down attention for image captioning and visual question answering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Later among the works it cites.
J. Gu, J. Cai, G. Wang, and T. Chen, “Stack-captioning: Coarse-to-fine learning for image captioning,” in Thirty-Second AAAI Conference on Artificial Intelligence
2018
Later among the works it cites.
T. Yao, Y. Pan, Y. Li, and T. Mei, “Exploring visual relationship for image captioning,” in Proceedings of the European Conference on Computer Vision (ECCV)
2018
Later among the works it cites.
J. Aneja, A. Deshpande, and A. G. Schwing, “Convolutional image captioning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Later among the works it cites.
S. Bai and S. An, “A survey on automatic image caption generation,” Neurocomputing
2018
Later among the works it cites.
J. Wäldchen and P. Mäder, “Machine learning for image based species identification,” Methods in Ecology and Evolution
2018
Later among the works it cites.
P. Tang, X. Wang, S. Bai, W. Shen, X. Bai, W. Liu, and A. L. Yuille, “Pcl: Proposal cluster learning for weakly supervised object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2018
Later among the works it cites.
Y. Chen, W. Zou, Y. Tang, X. Li, C. Xu, and N. Komodakis, “Scom: Spatiotemporal constrained optimization for salient object detection,” IEEE Transactions on Image Processing
2018
Later among the works it cites.
S. Li, B. Seybold, A. Vorobyov, X. Lei, and C.-C. Jay Kuo, “Unsupervised video object segmentation with motion-based bilateral networks,” in Proceedings of the European Conference on Computer Vision (ECCV)
2018
Later among the works it cites.
H. Song, W. Wang, S. Zhao, J. Shen, and K.-M. Lam, “Pyramid dilated deeper convlstm for video salient object detection,” in Proceedings of the European Conference on Computer Vision (ECCV)
2018
Later among the works it cites.
Y. Tang, W. Zou, Z. Jin, Y. Chen, Y. Hua, and X. Li, “Weakly supervised salient object detection with spatiotemporal cascade neural networks,” IEEE Transactions on Circuits and Systems for Video Technology
2018
Later among the works it cites.
Y. Liu, M. Cheng, X. Hu, J. Bian, L. Zhang, X. Bai, and J. Tang, “Richer convolutional features for edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2018
Later among the works it cites.
P. Lyu, C. Yao, W. Wu, S. Yan, and X. Bai, “Multi-oriented scene text detection via corner localization and region segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Later among the works it cites.
J. Ma, W. Shao, H. Ye, L. Wang, H. Wang, Y. Zheng, and X. Xue, “Arbitrary-oriented scene text detection via rotation proposals,” IEEE Transactions on Multimedia
2018
Later among the works it cites.
S.-A. Rebuffi, H. Bilen, and A. Vedaldi, “Efficient parametrization of multi-domain deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Later among the works it cites.
Y. Chen, W. Li, C. Sakaridis, D. Dai, and L. Van Gool, “Domain adaptive faster r-cnn for object detection in the wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Later among the works it cites.
2018
Later among the works it cites.
G. Bertasius, L. Torresani, and J. Shi, “Object detection in video with spatiotemporal sampling networks,” in Proceedings of the European Conference on Computer Vision (ECCV)
2018
Later among the works it cites.
F. Xiao and Y. Jae Lee, “Video object detection with an aligned spatial-temporal memory,” in Proceedings of the European Conference on Computer Vision (ECCV)
2018
Later among the works it cites.
Y. Zhou and O. Tuzel, “Voxelnet: End-to-end learning for point cloud based 3d object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Later among the works it cites.
Y. Chen, Z. Wang, Y. Peng, Z. Zhang, G. Yu, and J. Sun, “Cascaded pyramid network for multi-person pose estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Later among the works it cites.
B. Xiao, H. Wu, and Y. Wei, “Simple baselines for human pose estimation and tracking,” in Proceedings of the European Conference on Computer Vision (ECCV)
2018
Later among the works it cites.
X. He, Y. Peng, and J. Zhao, “Fast fine-grained image classification via weakly supervised discriminative localization,” IEEE Transactions on Circuits and Systems for Video Technology
2018
Later among the works it cites.
2019
Closest in time.
Z. Zou, Z. Shi, Y. Guo, and J. Ye, “Object detection in 20 years: A survey,” CoRR
2019
Closest in time.
2019
Closest in time.
M. Braun, S. Krebs, F. Flohr, and D. Gavrila, “Eurocity persons: A novel benchmark for person detection in traffic scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
K. Liang, H. Chang, B. Ma, S. Shan, and X. Chen, “Unifying visual attribute learning with object recognition in a multiplicative framework,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
C. Zhang and J. Kim, “Object detection with location-aware deformable convolution and backward attention filtering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
Y. He, C. Zhu, J. Wang, M. Savvides, and X. Zhang, “Bounding box regression with uncertainty for accurate object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2019
Closest in time.
C. Cabriel, N. Bourg, P. Jouchet, G. Dupuis, C. Leterrier, A. Baron, M.-A. Badet-Denisot, B. Vauzeilles, E. Fort, and S. Lévêque-Fort, “Combining 3d single molecule localization strategies for reproducible bioimaging,” Nature Communications
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” CoRR
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
R. Zhu, S. Zhang, X. Wang, L. Wen, H. Shi, L. Bo, and T. Mei, “Scratchdet: Training single-shot object detectors from scratch,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2019
Closest in time.
R. Ranjan, V. M. Patel, and R. Chellappa, “Hyperface: A deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
R. He, X. Wu, Z. Sun, and T. Tan, “Wasserstein cnn: Learning invariant features for nir-vis face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
2019
Closest in time.
Z. Cai, M. J. Saberian, and N. Vasconcelos, “Learning complexity-aware cascades for pedestrian detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
B. Barz, E. Rodner, Y. G. Garcia, and J. Denzler, “Detecting regions of maximal divergence for spatio-temporal anomaly detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
Y. Zhang, Y. Yuan, Y. Feng, and X. Lu, “Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection,” IEEE Transactions on Geoscience and Remote Sensing
2019
Closest in time.
Q. Li, L. Mou, Q. Xu, Y. Zhang, and X. X. Zhu, “R 3 -net: A deep network for multioriented vehicle detection in aerial images and videos,” IEEE Transactions on Geoscience and Remote Sensing
2019
Closest in time.
J. Pang, C. Li, J. Shi, Z. Xu, and H. Feng, “R 2 -cnn: Fast tiny object detection in large-scale remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing
2019
Closest in time.
W. Ma, Q. Guo, Y. Wu, W. Zhao, X. Zhang, and L. Jiao, “A novel multi-model decision fusion network for object detection in remote sensing images,” Remote Sensing
2019
Closest in time.
R. Dong, D. Xu, J. Zhao, L. Jiao, and J. An, “Sig-nms-based faster r-cnn combining transfer learning for small target detection in vhr optical remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing
2019
Closest in time.
C. Chen, C. He, C. Hu, H. Pei, and L. Jiao, “A deep neural network based on an attention mechanism for sar ship detection in multiscale and complex scenarios,” IEEE Access
2019
Closest in time.
H. Zhu, P. Zhang, L. Wang, X. Zhang, and L. Jiao, “A multiscale object detection approach for remote sensing images based on mse-densenet and the dynamic anchor assignment,” Remote Sensing Letters
2019
Closest in time.
M. Sarfraz and M. J. Ahmed, “An approach to license plate recognition system using neural network,” in Exploring Critical Approaches of Evolutionary Computation
2019
Closest in time.
W. Lu, Y. Zhou, G. Wan, S. Hou, S. Song, and B. A. D. B. U. ADU, “L3-net: Towards learning based lidar localization for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2019
Closest in time.
E. Arnold, O. Y. Al-Jarrah, M. Dianati, S. Fallah, D. Oxtoby, and A. Mouzakitis, “A survey on 3d object detection methods for autonomous driving applications,” IEEE Transactions on Intelligent Transportation Systems
2019
Closest in time.
Z. Li, M. Dong, S. Wen, X. Hu, P. Zhou, and Z. Zeng, “Clu-cnns: Object detection for medical images,” Neurocomputing
2019
Closest in time.
Q. Liu, L. Fang, G. Yu, D. Wang, C.-L. Xiao, and K. Wang, “Detection of dna base modifications by deep recurrent neural network on oxford nanopore sequencing data,” Nature Communications
2019
Closest in time.
P. J. Schubert, S. Dorkenwald, M. Januszewski, V. Jain, and J. Kornfeld, “Learning cellular morphology with neural networks,” Nature Communications
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
Z. Yang, Q. Li, L. Wenyin, and J. Lv, “Shared multi-view data representation for multi-domain event detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
W. Yang, R. T. Tan, J. Feng, J. Liu, S. Yan, and Z. Guo, “Joint rain detection and removal from a single image with contextualized deep networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
X. Hu, C. Fu, L. Zhu, J. Qin, and P. Heng, “Direction-aware spatial context features for shadow detection and removal,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
2019
Closest in time.
F. Wan, P. Wei, Z. Han, J. Jiao, and Q. Ye, “Min-entropy latent model for weakly supervised object detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
C. Cao, Y. Huang, Y. Yang, L. Wang, Z. Wang, and T. Tan, “Feedback convolutional neural network for visual localization and segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
W. Wang, J. Shen, X. Dong, A. Borji, and R. Yang, “Inferring salient objects from human fixations,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
L. Wang, L. Wang, H. Lu, P. Zhang, and X. Ruan, “Salient object detection with recurrent fully convolutional networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
M. Feng, H. Lu, and E. Ding, “Attentive feedback network for boundary-aware salient object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2019
Closest in time.
D.-P. Fan, W. Wang, M.-M. Cheng, and J. Shen, “Shifting more attention to video salient object detection,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
A. Haupmann, G. Kang, L. Jiang, and Y. Yang, “Contrastive adaptation network for unsupervised domain adaptation,” 2019
2019
Closest in time.
P. Tang, C. Wang, X. Wang, W. Liu, W. Zeng, and J. Wang, “Object detection in videos by high quality object linking,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.
2019
Closest in time.
G. Rogez, P. Weinzaepfel, and C. Schmid, “Lcr-net++: Multi-person 2d and 3d pose detection in natural images,” IEEE Transactions on Pattern Analysis and Machine Intelligence
2019
Closest in time.