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Recent advances in deep neural networks (DNNs) have led to object detectors that can rapidly process pictures or videos, and recognize the objects that they contain.
Metamorphic testing: a new approach for generating next test cases
Tsong Y Chen, Shing C Cheung, and Shiu Ming Yiu. 1998 · 1998
Earlier work this paper cites.
Statistics of natural images and models. In Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149) , Vol. 1. IEEE, 541–547
Jinggang Huang and David Mumford. 1999 · 1999
Earlier work this paper cites.
Yesterday, My Program Worked. Today, It Does Not. Why?. In Proceedings of the 7th European Software Engineering Conference Held Jointly with the 7th ACM SIGSOFT International Symposium on Foundations of Software Engineering (ESEC/FSE-7) . 253–267
Andreas Zeller. 1999 · 1999
Earlier work this paper cites.
A Novel Method for Video Tracking Performance Evaluation. In In Joint IEEE Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS . 125–132
James Black, Tim Ellis, and Paul Rosin. 2003 · 2003
Earlier work this paper cites.
Histograms of oriented gradients for human detection
Navneet Dalal and Bill Triggs. 2005 · 2005
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan and Qiang Yang. 2009 · 2009
Earlier work this paper cites.
An HOG-LBP human detector with partial occlusion handling. In 2009 IEEE 12th international conference on computer vision . IEEE, 32–39
Xiaoyu Wang, Tony X Han, and Shuicheng Yan. 2009 · 2009
Earlier work this paper cites.
The pascal visual object classes (voc) challenge
Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. 2010 · 2010
Earlier work this paper cites.
Improving the Fisher Kernel for Large-scale Image Classification. In Proceedings of the 11th European Conference on Computer Vision: Part IV (ECCV’10) . 143–156
Florent Perronnin, Jorge Sánchez, and Thomas Mensink. 2010 · 2010
Earlier work this paper cites.
Average Hash
Neal Krawetz. 2011 · 2011
Earlier work this paper cites.
Selective Search for Object Recognition
J. R. Uijlings, K. E. Sande, T. Gevers, and A. W. Smeulders. 2013 · 2013
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross B. Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. 2014 · 2014
Earlier work this paper cites.
Generative adversarial nets. In Advances in neural information processing systems . 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Compiler Validation via Equivalence Modulo Inputs. In Proceedings of the 35th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’14) . 216–226
Vu Le, Mehrdad Afshari, and Zhendong Su. 2014 · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context. In European conference on computer vision . Springer, 740–755
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
Exploring Invariances in Deep Convolutional Neural Networks Using Synthetic Images
Xingchao Peng, Baochen Sun, Karim Ali, and Kate Saenko. 2014 · 2014
Earlier work this paper cites.
On Rendering Synthetic Images for Training an Object Detector
Artem Rozantsev, Vincent Lepetit, and Pascal Fua. 2014 · 2014
Earlier work this paper cites.
Fast R-CNN
Ross B. Girshick. 2015 · 2015
Earlier work this paper cites.
Understanding deep image representations by inverting them. In Proceedings of the IEEE conference on computer vision and pattern recognition . 5188–5196
Aravindh Mahendran and Andrea Vedaldi. 2015 · 2015
Earlier work this paper cites.
Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE international conference on computer vision . 1520–1528
Hyeonwoo Noh, Seunghoon Hong, and Bohyung Han. 2015 · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems . 91–99
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015 · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
TensorFlow: A System for Large-scale Machine Learning. In Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation (OSDI’16) . USENIX Association, Berkeley, CA, USA, 265–283
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016 · 2016
Earlier work this paper cites.
R-fcn: Object detection via region-based fully convolutional networks. In Advances in neural information processing systems . 379–387
Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Virtual worlds as proxy for multi-object tracking analysis. In Proceedings of the IEEE conference on computer vision and pattern recognition . 4340–4349
Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
SSD: Single shot multibox detector. In European conference on computer vision . Springer, 21–37
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. 2016 · 2016
Earlier work this paper cites.
Visualizing deep convolutional neural networks using natural pre-images
Aravindh Mahendran and Andrea Vedaldi. 2016 · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2536–2544
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. 2016 · 2016
Earlier work this paper cites.
A comprehensive evaluation of cryptographic algorithms: DES, 3DES, AES, RSA and Blowfish
Priyadarshini Patil, Prashant Narayankar, DG Narayan, and S Md Meena. 2016 · 2016
Cited alongside, same era.
You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition . 779–788
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016 · 2016
Cited alongside, same era.
Recurrent instance segmentation. In European conference on computer vision . Springer, 312–329
Bernardino Romera-Paredes and Philip Hilaire Sean Torr. 2016 · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition . 2818–2826
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016 · 2016
Cited alongside, same era.
Tesla’s Self-Driving System Cleared in Deadly Crash
Neal E. Boudette. 2017 · 2017
Deep Learning for Generic Object Detection: A Survey
Li Liu, Wanli Ouyang, Xiaogang Wang, Paul W. Fieguth, Jie Chen, Xinwang Liu, and Matti Pietikäinen. 2018 · 2018
Later among the works it cites.
DeepGauge: Comprehensive and multi-granularity testing criteria for gauging the robustness of deep learning systems. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE 2018)
Lei Ma, Felix Juefei-Xu, Jiyuan Sun, Chunyang Chen, Ting Su, Fuyuan Zhang, Minhui Xue, Bo Li, Li Li, Yang Liu, et al · 2018
Later among the works it cites.
Deepmutation: Mutation testing of deep learning systems. In 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 100–111
Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, et al · 2018
Later among the works it cites.
Tensorfuzz: Debugging neural networks with coverage-guided fuzzing
Augustus Odena and Ian Goodfellow. 2018 · 2018
Later among the works it cites.
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Cited alongside, same era.
Cut, paste and learn: Surprisingly easy synthesis for instance detection. In Proceedings of the IEEE International Conference on Computer Vision . 1301–1310
Debidatta Dwibedi, Ishan Misra, and Martial Hebert. 2017 · 2017
Cited alongside, same era.
Synthesizing training data for object detection in indoor scenes
Georgios Georgakis, Arsalan Mousavian, Alexander C Berg, and Jana Kosecka. 2017 · 2017
Cited alongside, same era.
Mask R-CNN. In Proceedings of the IEEE international conference on computer vision . 2961–2969
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017 · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017 · 2017
Cited alongside, same era.
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2017 · 2017
Cited alongside, same era.
Fully convolutional instance-aware semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2359–2367
Yi Li, Haozhi Qi, Jifeng Dai, Xiangyang Ji, and Yichen Wei. 2017 · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch. In NIPS-W
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
Cited alongside, same era.
Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data
Aayush Prakash, Shaad Boochoon, Mark Brophy, David Acuna, Eric Cameracci, Gavriel State, Omer Shapira, and Stan Birchfield. 2018 · 2018
Later among the works it cites.
Human-centric indoor scene synthesis using stochastic grammar. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 5899–5908
Siyuan Qi, Yixin Zhu, Siyuan Huang, Chenfanfu Jiang, and Song-Chun Zhu. 2018 · 2018
Later among the works it cites.
YOLOv3: An Incremental Improvement
Joseph Redmon and Ali Farhadi. 2018 · 2018
Later among the works it cites.
Concolic Testing for Deep Neural Networks. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE 2018) . 109–119
Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, and Daniel Kroening. 2018 · 2018
Later among the works it cites.
DeepTest: Automated Testing of Deep-neural-network-driven Autonomous Cars. In Proceedings of the 40th International Conference on Software Engineering (ICSE ’18) . 303–314
Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray. 2018 · 2018
Later among the works it cites.
Training deep networks with synthetic data: Bridging the reality gap by domain randomization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops . 969–977
Jonathan Tremblay, Aayush Prakash, David Acuna, Mark Brophy, Varun Jampani, Cem Anil, Thang To, Eric Cameracci, Shaad Boochoon, and Stan Birchfield. 2018 · 2018
Later among the works it cites.
Automated Directed Fairness Testing. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE 2018) . 98–108
Sakshi Udeshi, Pryanshu Arora, and Sudipta Chattopadhyay. 2018 · 2018
Later among the works it cites.
Box2pix: Single-shot instance segmentation by assigning pixels to object boxes. In 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 292–299
Jonas Uhrig, Eike Rehder, Björn Fröhlich, Uwe Franke, and Thomas Brox. 2018 · 2018
Later among the works it cites.
Coverage-guided fuzzing for deep neural networks
Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Hongxu Chen, Minhui Xue, Bo Li, Yang Liu, Jianjun Zhao, Jianxiong Yin, and Simon See. 2018 · 2018
Later among the works it cites.
BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling
Fisher Yu, Wenqi Xian, Yingying Chen, Fangchen Liu, Mike Liao, Vashisht Madhavan, and Trevor Darrell. 2018 · 2018
Later among the works it cites.
DeepRoad: GAN-based Metamorphic Testing and Input Validation Framework for Autonomous Driving Systems. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE 2018)
Mengshi Zhang, Yuqun Zhang, Lingming Zhang, Cong Liu, and Sarfraz Khurshid. 2018b · 2018
Later among the works it cites.
An Empirical Study on TensorFlow Program Bugs. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2018) . 129–140
Yuhao Zhang, Yifan Chen, Shing-Chi Cheung, Yingfei Xiong, and Lu Zhang. 2018a · 2018
Later among the works it cites.
Object Detection with Deep Learning: A Review
Zhong-Qiu Zhao, Peng Zheng, Shou-tao Xu, and Xindong Wu. 2018 · 2018
Later among the works it cites.
Azure Computer Vision API
2019 · 2019
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berkeley DeepDrive
2019 · 2019
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Dropbox folder of all erroneous detection results found by MetaOD
2019 · 2019
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IBM Vision API
2019 · 2019
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Strike (with) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects (CVPR 2019)
Michael A. Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, and Anh Nguyen. 2019 · 2019
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YOLACT: Real-time Instance Segmentation
Daniel Bolya, Chong Zhou, Fanyi Xiao, and Yong Jae Lee. 2019 · 2019
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DeepStellar: Model-based Quantitative Analysis of Stateful Deep Learning Systems. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019) . 477–487
Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, and Jianjun Zhao. 2019 · 2019
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Guiding Deep Learning System Testing Using Surprise Adequacy. In Proceedings of the 41st International Conference on Software Engineering (ICSE ’19) . 1039–1049
Jinhan Kim, Robert Feldt, and Shin Yoo. 2019 · 2019
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CRADLE: Cross-Backend Validation to Detect and Localize Bugs in Deep Learning Libraries. In Proceedings of the 41st International Conference on Software Engineering (ICSE ’19)
Hung Viet Pham, Thibaud Lutellier, Weizhen Qi, and Lin Tan. 2019 · 2019
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DeepConcolic: Testing and Debugging Deep Neural Networks. In Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings (ICSE ’19) . 111–114
Youcheng Sun, Xiaowei Huang, Daniel Kroening, James Sharp, Matthew Hill, and Rob Ashmore. 2019 · 2019
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Adversarial Sample Detection for Deep Neural Network Through Model Mutation Testing. In Proceedings of the 41st International Conference on Software Engineering (ICSE ’19) . 1245–1256
Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, and Peixin Zhang. 2019 · 2019
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