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Recently, there has been an increasing concern about the privacy issue raised by using personally identifiable information in machine learning.
A closed-form solution to natural image matting
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Model inversion attacks that exploit confidence information and basic countermeasures. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security . 1322–1333
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Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015 · 2015
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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
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Deep automatic portrait matting. In Proceedings of the European Conference on Computer Vision . 92–107
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Designing effective inter-pixel information flow for natural image matting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 29–37
Yagiz Aksoy, Tunc Ozan Aydin, and Marc Pollefeys. 2017 · 2017
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How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks). In International Conference on Computer Vision
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Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP) . IEEE
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Deep image matting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2970–2979
Ning Xu, Brian Price, Scott Cohen, and Thomas Huang. 2017 · 2017
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Semantic soft segmentation
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Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?
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Auditing Differentially Private Machine Learning: How Private is Private SGD?. In Advances in Neural Information Processing Systems , Vol. 33. Curran Associates, Inc., 22205–22216
Matthew Jagielski, Jonathan Ullman, and Alina Oprea. 2020 · 2020
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Natural image matting via guided contextual attention. In Proceedings of the AAAI Conference on Artificial Intelligence
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Semantic human matting. In Proceedings of the ACM International Conference on Multimedia . 618–626
Quan Chen, Tiezheng Ge, Yanyu Xu, Zhiqiang Zhang, Xinxin Yang, and Kun Gai. 2018 · 2018
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AlphaGAN: Generative adversarial networks for natural image matting. In British Machine Vision Conference 2018, BMVC 2018, Newcastle, UK, September 3-6, 2018 . BMVA Press, 259
Sebastian Lutz, Konstantinos Amplianitis, and Aljosa Smolic. 2018 · 2018
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Deep face recognition: A survey. In 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI) . IEEE, 471–478
Iacopo Masi, Yue Wu, Tal Hassner, and Prem Natarajan. 2018 · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 19)
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
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Qiqi Hou and Feng Liu. 2019 · 2019
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Indices Matter: Learning to Index for Deep Image Matting. In Proceedings of the IEEE International Conference on Computer Vision . 3266–3275
Hao Lu, Yutong Dai, Chunhua Shen, and Songcen Xu. 2019 · 2019
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Deep high-resolution representation learning for human pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 5693–5703
Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. 2019 · 2019
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Shanchuan Lin, Andrey Ryabtsev, Soumyadip Sengupta, Brian Curless, Steve Seitz, and Ira Kemelmacher-Shlizerman. 2020 · 2020
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Boosting Semantic Human Matting with Coarse Annotations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 8563–8572
Jinlin Liu, Yuan Yao, Wendi Hou, Miaomiao Cui, Xuansong Xie, Changshui Zhang, and Xian-sheng Hua. 2020 · 2020
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Attention-Guided Hierarchical Structure Aggregation for Image Matting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
Yu Qiao, Yuhao Liu, Xin Yang, Dongsheng Zhou, Mingliang Xu, Qiang Zhang, and Xiaopeng Wei. 2020 · 2020
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Background Matting: The World is Your Green Screen. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2291–2300
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Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things
Jing Zhang and Dacheng Tao. 2020 · 2020
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Deep Automatic Natural Image Matting. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 . International Joint Conferences on Artificial Intelligence Organization
Jizhizi Li, Jing Zhang, and Dacheng Tao. 2021 · 2021
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A Study of Face Obfuscation in ImageNet
Kaiyu Yang, Jacqueline Yau, Li Fei-Fei, Jia Deng, and Olga Russakovsky. 2021 · 2021
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Mask Guided Matting via Progressive Refinement Network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1154–1163
Qihang Yu, Jianming Zhang, He Zhang, Yilin Wang, Zhe Lin, Ning Xu, Yutong Bai, and Alan Yuille. 2021 · 2021
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Towards high performance human keypoint detection
Jing Zhang, Zhe Chen, and Dacheng Tao. 2021 · 2021
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Privacy-preserving visual learning using doubly permuted homomorphic encryption. In Proceedings of the IEEE International Conference on Computer Vision . 2040–2050
Ryo Yonetani, Vishnu Naresh Boddeti, Kris M Kitani, and Yoichi Sato. 2017 · 2050
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A global sampling method for alpha matting. In CVPR 2011 . IEEE, 2049–2056
Kaiming He, Christoph Rhemann, Carsten Rother, Xiaoou Tang, and Jian Sun. 2011 · 2056
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