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There are a huge number of features which are said to improve Convolutional Neural Network (CNN) accuracy.
Example-based learning for view-based human face detection
K-K Sung and Tomaso Poggio · 1998
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Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce · 2006
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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Regularization of neural networks using DropConnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann Le Cun, and Rob Fergus · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
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Microsoft COCO: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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DropOut: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Fast R-CNN
Ross Girshick · 2015
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Hypercolumns for object segmentation and fine-grained localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik · 2015
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Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Faster R-CNN: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Efficient object localization using convolutional networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler · 2015
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R-FCN: Object detection via region-based fully convolutional networks
Jifeng Dai, Yi Li, Kaiming He, and Jian Sun · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 MB model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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FractalNet: Ultra-deep neural networks without residuals
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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SSD: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Gupta, and Ross Girshick · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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UnitBox: An advanced object detection network
Jiahui Yu, Yuning Jiang, Zhangyang Wang, Zhimin Cao, and Thomas Huang · 2016
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Soft-NMS–improving object detection with one line of code
Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S Davis · 2017
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DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille · 2017
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Improved regularization of convolutional neural networks with CutOut
Terrance DeVries and Graham W Taylor · 2017
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Mask R-CNN
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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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
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Label refinement network for coarse-to-fine semantic segmentation
Md Amirul Islam, Shujon Naha, Mrigank Rochan, Neil Bruce, and Yang Wang · 2017
Cited alongside, same era.
Self-normalizing neural networks
Günter Klambauer, Thomas Unterthiner, Andreas Mayr, and Sepp Hochreiter · 2017
Cited alongside, same era.
Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
Cited alongside, same era.
Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
Cited alongside, same era.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le · 2017
Cited alongside, same era.
YOLO9000: better, faster, stronger
Searching for MobileNetV3
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, et al · 2019
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CornerNet-Lite: Efficient keypoint based object detection
Hei Law, Yun Teng, Olga Russakovsky, and Jia Deng · 2019
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Dynamic anchor feature selection for single-shot object detection
Shuai Li, Lingxiao Yang, Jianqiang Huang, Xian-Sheng Hua, and Lei Zhang · 2019
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Scale-aware trident networks for object detection
Yanghao Li, Yuntao Chen, Naiyan Wang, and Zhaoxiang Zhang · 2019
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Learning spatial fusion for single-shot object detection
Songtao Liu, Di Huang, and Yunhong Wang · 2019
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Joseph Redmon and Ali Farhadi · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2017
Cited alongside, same era.
MixUp: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
Cited alongside, same era.
Random erasing data augmentation
Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang · 2017
Cited alongside, same era.
Cascade R-CNN: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
Cited alongside, same era.
DropBlock: A regularization method for convolutional networks
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
Cited alongside, same era.
Diganta Misra · 2019
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Enriched feature guided refinement network for object detection
Jing Nie, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang, and Ling Shao · 2019
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Libra R-CNN: Towards balanced learning for object detection
Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, and Dahua Lin · 2019
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Matrix Nets: A new deep architecture for object detection
Abdullah Rashwan, Agastya Kalra, and Pascal Poupart · 2019
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Generalized intersection over union: A metric and a loss for bounding box regression
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, and Silvio Savarese · 2019
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Saurabh Singh and Shankar Krishnan · 2019
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MNASnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
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EfficientNet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc V Le · 2019
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MixNet: Mixed depthwise convolutional kernels
Mingxing Tan and Quoc V Le · 2019
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FCOS: Fully convolutional one-stage object detection
Zhi Tian, Chunhua Shen, Hao Chen, and Tong He · 2019
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Region proposal by guided anchoring
Jiaqi Wang, Kai Chen, Shuo Yang, Chen Change Loy, and Dahua Lin · 2019
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RDSNet: A new deep architecture for reciprocal object detection and instance segmentation
Shaoru Wang, Yongchao Gong, Junliang Xing, Lichao Huang, Chang Huang, and Weiming Hu · 2019
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Learning rich features at high-speed for single-shot object detection
Tiancai Wang, Rao Muhammad Anwer, Hisham Cholakkal, Fahad Shahbaz Khan, Yanwei Pang, and Ling Shao · 2019
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RepPoints: Point set representation for object detection
Ze Yang, Shaohui Liu, Han Hu, Liwei Wang, and Stephen Lin · 2019
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CutMix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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FreeAnchor: Learning to match anchors for visual object detection
Xiaosong Zhang, Fang Wan, Chang Liu, Rongrong Ji, and Qixiang Ye · 2019
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M2det: A single-shot object detector based on multi-level feature pyramid network
Qijie Zhao, Tao Sheng, Yongtao Wang, Zhi Tang, Ying Chen, Ling Cai, and Haibin Ling · 2019
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Soft anchor-point object detection
Chenchen Zhu, Fangyi Chen, Zhiqiang Shen, and Marios Savvides · 2019
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Feature selective anchor-free module for single-shot object detection
Chenchen Zhu, Yihui He, and Marios Savvides · 2019
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Pengguang Chen · 2020
Closest in time.
Hit-Detector: Hierarchical trinity architecture search for object detection
Jianyuan Guo, Kai Han, Yunhe Wang, Chao Zhang, Zhaohui Yang, Han Wu, Xinghao Chen, and Chang Xu · 2020
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GhostNet: More features from cheap operations
Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu · 2020
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CenterMask: Real-time anchor-free instance segmentation
Youngwan Lee and Jongyoul Park · 2020
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EfficientDet: Scalable and efficient object detection
Mingxing Tan, Ruoming Pang, and Quoc V Le · 2020
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CSPNet: A new backbone that can enhance learning capability of cnn
Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh · 2020
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SM-NAS: Structural-to-modular neural architecture search for object detection
Lewei Yao, Hang Xu, Wei Zhang, Xiaodan Liang, and Zhenguo Li · 2020
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Cross-iteration batch normalization
Zhuliang Yao, Yue Cao, Shuxin Zheng, Gao Huang, and Stephen Lin · 2020
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Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection
Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, and Stan Z Li · 2020
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Distance-IoU Loss: Faster and better learning for bounding box regression
Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren · 2020
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