Fetching the paper…
Reading the bibliography…
Scale-permuted networks have shown promising results on object bounding box detection and instance segmentation.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Semantic contours from inverse detectors
Bharath Hariharan, Pablo Arbelaez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik · 2011
Earlier work this paper cites.
Adaptive deconvolutional networks for mid and high level feature learning
M. D. Zeiler, G. W. Taylor, and R. Fergus · 2011
Earlier work this paper cites.
The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
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
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
Liang-Chieh Chen, G. Papandreou, I. Kokkinos, Kevin Murphy, and A. Yuille · 2015
Earlier work this paper cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Learning deconvolution network for semantic segmentation
Hyeonwoo Noh, Seunghoon Hong, and B. Han · 2015
Earlier work this paper cites.
Modeling local and global deformations in deep learning: Epitomic convolution, multiple instance learning, and sliding window detection
G. Papandreou, I. Kokkinos, and P. Savalle · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Earlier work this paper cites.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Bridging category-level and instance-level semantic image segmentation
Zifeng Wu, Chunhua Shen, and A. V. D. Hengel · 2016
Earlier work this paper cites.
Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
Earlier work this paper cites.
Wide residual networks
Sergey Zagoruyko and Nikos Komodakis · 2016
Cited alongside, same era.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Vijay Badrinarayanan, Alex Kendall, and R. Cipolla · 2017
Cited alongside, same era.
Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, G. Papandreou, Florian Schroff, and H. Adam · 2017
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
François Chollet · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 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
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Later among the works it cites.
Randaugment: Practical automated data augmentation with a reduced search space, 2019
Ekin D. Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V. Le · 2019
Later among the works it cites.
Nas-fpn: Learning scalable feature pyramid architecture for object detection
Golnaz Ghiasi, Tsung-Yi Lin, and Quoc V Le · 2019
Later among the works it cites.
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
Later among the works it cites.
Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Chenxi Liu, Liang-Chieh Chen, Florian Schroff, Hartwig Adam, Wei Hua, Alan L Yuille, and Li Fei-Fei · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Large kernel matters–improve semantic segmentation by global convolutional network
Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, and Jian Sun · 2017
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Evan Shelhamer, J. Long, and Trevor Darrell · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 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.
Scale-adaptive convolutions for scene parsing
R. Zhang, S. Tang, Y. Zhang, J. Li, and S. Yan · 2017
Cited alongside, same era.
Pyramid scene parsing network
Hengshuang Zhao, J. Shi, Xiaojuan Qi, Xiaogang Wang, and J. Jia · 2017
Cited alongside, same era.
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le · 2019
Later among the works it cites.
Squeezenas: Fast neural architecture search for faster semantic segmentation
Albert Eaton Shaw, D. Hunter, Forrest N. Iandola, and S. Sidhu · 2019
Later among the works it cites.
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
Later among the works it cites.
NAS-FCOS: Fast neural architecture search for object detection
Ning Wang, Yang Gao, Hao Chen, Peng Wang, Zhi Tian, and Chunhua Shen · 2019
Later among the works it cites.
Auto-fpn: Automatic network architecture adaptation for object detection beyond classification
Hang Xu, Lewei Yao, Wei Zhang, Xiaodan Liang, and Zhenguo Li · 2019
Later among the works it cites.
Multiscale deep equilibrium models
Shaojie Bai, Vladlen Koltun, and J. Zico Kolter · 2020
Later among the works it cites.
Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation
Bowen Cheng, Maxwell D. Collins, Y. Zhu, T. Liu, T. Huang, H. Adam, and Liang-Chieh Chen · 2020
Later among the works it cites.
Efficient scale-permuted backbone with learned resource distribution
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, M. Tan, Quoc V. Le, and Xiaodan Song · 2020
Later among the works it cites.
Spinenet: Learning scale-permuted backbone for recognition and localization
Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, and Xiaodan Song · 2020
Later among the works it cites.
Computation reallocation for object detection
Feng Liang, Chen Lin, Ronghao Guo, Ming Sun, Wei Wu, J. Yan, and Wanli Ouyang · 2020
Later among the works it cites.
Deep high-resolution representation learning for visual recognition
Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al · 2020
Later among the works it cites.
Resnest: Split-attention networks, 2020
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, and Alexander Smola · 2020
Later among the works it cites.
Rethinking pre-training and self-training
Barret Zoph, G. Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, E. D. Cubuk, and Quoc V. Le · 2020
Later among the works it cites.
Repvgg: Making vgg-style convnets great again
Xiaohan Ding, X. Zhang, Ningning Ma, J. Han, G. Ding, and Jian Sun · 2021
Closest in time.