Acknowledgement
Grant : 객체 추출 및 실-가상 정합 지원 모바일 AR 기술 개발
Supported by : 정보통신기획평가원
References
- Uijlings, Jasper RR et al., "Selective search for object recognition," International journal of computer vision 104.2 (2013): 154-171. https://doi.org/10.1007/s11263-013-0620-5
- Ren, Shaoqing et al., "Faster r-cnn: Towards real-time object detection with region proposal networks," Advances in neural information processing systems. 2015.
- Liu, Wei et al., "Ssd: Single shot multibox detector," European conference on computer vision. Springer, Cham, 2016.
- Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556 (2014).
- Sandler, Mark et al., "Mobilenetv2: Inverted residuals and linear bottlenecks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
- Redmon, Joseph et al., "You only look once: Unified, real-time object detection," Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger," Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767 (2018).
- Howard, Andrew G. et al., "Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861 (2017).
- Howard, Andrew et al., "Searching for mobilenetv3," arXiv preprint arXiv:1905.02244 (2019).
- Zhang, Xiangyu et al., "Shufflenet: An extremely efficient convolutional neural network for mobile devices," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
- Ma, Ningning et al., "Shufflenet v2: Practical guidelines for efficient cnn architecture design," Proceedings of the European Conference on Computer Vision (ECCV). 2018.
- Iandola, Forrest N. et al., "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size," arXiv preprint arXiv:1602.07360 (2016).
- Wu, Bichen et al., "Shift: A zero flop, zero parameter alternative to spatial convolutions," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
- Yang, Tien-Ju et al., "Netadapt: Platform-aware neural network adaptation for mobile applications," Proceedings of the European Conference on Computer Vision (ECCV). 2018.
- LeCun, Yann et al., "Gradient-based learning applied to document recognition," Proceedings of the IEEE 86.11 (1998): 2278-2324. https://doi.org/10.1109/5.726791
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems. 2012.
- Jeon, Yunho, and Junmo Kim. "Constructing fast network through deconstruction of convolution," Advances in Neural Information Processing Systems. 2018.
- Chen, Weijie et al., "All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification," arXiv preprint arXiv:1903.05285 (2019).
- Tan, Mingxing et al., "Mnasnet: Platform-aware neural architecture search for mobile," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
- Wu, Bichen et al., "Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
- He, Yihui et al., "Amc: Automl for model compression and acceleration on mobile devices," Proceedings of the European Conference on Computer Vision (ECCV). 2018.
- Wang, Robert J., Xiang Li, and Charles X. Ling. "Pelee: A real-time object detection system on mobile devices," Advances in Neural Information Processing Systems. 2018.
- Yang, Yifan et al., "Synetgy: Algorithm-hardware co-design for convnet accelerators on embedded fpgas," Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, 2019.
- Nakahara, Hiroki et al., "A lightweight yolov2: A binarized cnn with a parallel support vector regression for an fpga," Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. ACM, 2018.
- Alyamkin, Sergei et al., "Low-Power Computer Vision: Status, Challenges, Opportunities," IEEE Journal on Emerging and Selected Topics in Circuits and Systems (2019).
- https://rebootingcomputing.ieee.org/lpirc/2018
- Caesar, Holger, Jasper Uijlings, and Vittorio Ferrari. "Coco-stuff: Thing and stuff classes in context," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018.
- Kirillov, Alexander et al., "Panoptic segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
- Everingham, Mark et al., "The pascal visual object classes (voc) challenge," International journal of computer vision 88.2 (2010): 303-338. https://doi.org/10.1007/s11263-009-0275-4
- Lin, Tsung-Yi et al., "Microsoft coco: Common objects in context," European conference on computer vision. Springer, Cham, 2014.
- Cordts, Marius et al., "The cityscapes dataset for semantic urban scene understanding," Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- Zhou, Bolei et al., "Scene parsing through ade20k dataset," Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- Neuhold, Gerhard et al., "The mapillary vistas dataset for semantic understanding of street scenes," Proceedings of the IEEE International Conference on Computer Vision. 2017.
- Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
- Noh, Hyeonwoo, Seunghoon Hong, and Bohyung Han. "Learning deconvolution network for semantic segmentation," Proceedings of the IEEE international conference on computer vision. 2015.
- Chen, Liang-Chieh et al., "Semantic image segmentation with deep convolutional nets and fully connected crfs," arXiv preprint arXiv:1412.7062 (2014).
- Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv: 1511.07122 (2015).
- Zhao, Hengshuang et al., "Pyramid scene parsing network," Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- Chen, Liang-Chieh et al., "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs," IEEE transactions on pattern analysis and machine intelligence 40.4 (2017): 834-848. https://doi.org/10.1109/TPAMI.2017.2699184
- Lazebnik, Svetlana, Cordelia Schmid, and Jean Ponce. "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). Vol. 2. IEEE, 2006.
- Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation," International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
- Badrinarayanan, Vijay, Alex Kendall, and Roberto Cipolla. "Segnet: A deep convolutional encoder-decoder architecture for image segmentation," IEEE transactions on pattern analysis and machine intelligence 39.12 (2017): 2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615
- Lin, Guosheng et al., "Refinenet: Multi-path refinement networks for high-resolution semantic segmentation," Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
- Paszke, Adam et al., "Enet: A deep neural network architecture for real-time semantic segmentation," arXiv preprint arXiv:1606.02147 (2016).
- Chen, Liang-Chieh et al., "Encoder-decoder with atrous separable convolution for semantic image segmentation," Proceedings of the European conference on computer vision (ECCV). 2018.
- He, Kaiming et al., "Mask r-cnn," Proceedings of the IEEE international conference on computer vision. 2017.
- Cai, Zhaowei, and Nuno Vasconcelos. "Cascade r-cnn: Delving into high quality object detection," Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
- Chen, Kai et al., "Hybrid task cascade for instance segmentation," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.
- COCO+Mapillary Joint Recognition Challenge Workshop at ECCV 2018, http://cocodataset.org/workshop/coco-mapillary-eccv-2018.html
- Fu, Cheng-Yang, Mykhailo Shvets, and Alexander C. Berg. "RetinaMask: Learning to predict masks improves state-ofthe-art single-shot detection for free," arXiv preprint arXiv:1901.03353 (2019).
- Lin, Tsung-Yi et al., "Focal loss for dense object detection," Proceedings of the IEEE international conference on computer vision. 2017.
- Chen, Liang-Chieh et al., "Rethinking atrous convolution for semantic image segmentation," arXiv preprint arXiv:1706.05587 (2017).
- Apple Core ML Models: DeeplabV3, https://developer.apple.com/machine-learning/models/#image
- Mobile Deeplab-V3+ model for Segmentation, https://github.com/nolanliou/mobile-deeplab-v3-plus