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Keypoint-based methods are a relatively new paradigm in object detection, eliminating the need for anchor boxes and offering a simplified detection framework.
Deformable convnets v2: More deformable, better results
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Rapid object detection using a boosted cascade of simple features
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Real-time accurate object detection using multiple resolutions
Wei Zhang, Gregory Zelinsky, and Dimitris Samaras · 2007
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Multistage particle windows for fast and accurate object detection
Giovanni Gualdi, Andrea Prati, and Rita Cucchiara · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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Eye movements and vision
Alfred L Yarbus · 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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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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Fast r-cnn
Ross Girshick · 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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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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A unified multi-scale deep convolutional neural network for fast object detection
Zhaowei Cai, Quanfu Fan, Rogerio S Feris, and Nuno Vasconcelos · 2016
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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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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 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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Stacked hourglass networks for human pose estimation
Alejandro Newell, Kaiyu Yang, and Jia Deng · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 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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Beyond skip connections: Top-down modulation for object detection
Denet: Scalable real-time object detection with directed sparse sampling
Lachlan Tychsen-Smith and Lars Petersson · 2017
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Point linking network for object detection
Xinggang Wang, Kaibing Chen, Zilong Huang, Cong Yao, and Wenyu Liu · 2017
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Cascade r-cnn: Delving into high quality object detection
Zhaowei Cai and Nuno Vasconcelos · 2018
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Revisiting rcnn: On awakening the classification power of faster rcnn
Bowen Cheng, Yunchao Wei, Honghui Shi, Rogerio Feris, Jinjun Xiong, and Thomas Huang · 2018
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Squeeze-and-excitation networks
Jie Hu, Li Shen, and Gang Sun · 2018
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Acquisition of localization confidence for accurate object detection
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Abhinav Shrivastava, Rahul Sukthankar, Jitendra Malik, and Abhinav Gupta · 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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Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
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Deformable convolutional networks
Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei · 2017
Cited alongside, same era.
Dssd: Deconvolutional single shot detector
Cheng-Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, and Alexander C Berg · 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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Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, and Yuning Jiang · 2018
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Shufflenasnets: Efficient cnn models through modified efficient neural architecture search
Kevin Alexander Laube and Andreas Zell · 2018
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Cornernet: Detecting objects as paired keypoints
Hei Law and Jia Deng · 2018
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Receptive field block net for accurate and fast object detection
Songtao Liu, Di Huang, et al · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun · 2018
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Autofocus: Efficient multi-scale inference
Mahyar Najibi, Bharat Singh, and Larry S Davis · 2018
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Megdet: A large mini-batch object detector
Chao Peng, Tete Xiao, Zeming Li, Yuning Jiang, Xiangyu Zhang, Kai Jia, Gang Yu, and Jian Sun · 2018
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Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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An analysis of scale invariance in object detection snip
Bharat Singh and Larry S Davis · 2018
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Sniper: Efficient multi-scale training
Bharat Singh, Mahyar Najibi, and Larry S Davis · 2018
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Pelee: A real-time object detection system on mobile devices
Robert J Wang, Xiang Li, and Charles X Ling · 2018
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Deep regionlets for object detection
Hongyu Xu, Xutao Lv, Xiaoyu Wang, Zhou Ren, Navaneeth Bodla, and Rama Chellappa · 2018
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Feature selective networks for object detection
Yao Zhai, Jingjing Fu, Yan Lu, and Houqiang Li · 2018
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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 · 2018
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