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3D Convolution Neural Networks (CNNs) have been widely applied to 3D scene understanding, such as video analysis and volumetric image recognition.
Simpson, A. L.; Antonelli, M.; Bakas, S.; Bilello, M.; Farahani, K.; van Ginneken, B.; Kopp-Schneider, A.; Landman, B. A.; Litjens, G.; Menze, B.; et al. 2019 · 1902
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Single path one-shot neural architecture search with uniform sampling
Guo, Z.; Zhang, X.; Mu, H.; Heng, W.; Liu, Z.; Wei, Y.; and Sun, J. 2019 · 1904
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Single-path nas: Designing hardware-efficient convnets in less than 4 hours
Stamoulis, D.; Ding, R.; Wang, D.; Lymberopoulos, D.; Priyantha, B.; Liu, J.; and Marculescu, D. 2019 · 1904
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Assemblenet: Searching for multi-stream neural connectivity in video architectures
Ryoo, M. S.; Piergiovanni, A.; Tan, M.; and Angelova, A. 2019 · 1905
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2012 · 2012
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Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation
Roth, H. R.; Lu, L.; Farag, A.; Shin, H.-C.; Liu, J.; Turkbey, E. B.; and Summers, R. M. 2015 · 2015
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Learning spatiotemporal features with 3d convolutional networks
Tran, D.; Bourdev, L.; Fergus, R.; Torresani, L.; and Paluri, M. 2015 · 2015
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3D U-Net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö.; Abdulkadir, A.; Lienkamp, S. S.; Brox, T.; and Ronneberger, O. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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V-net: Fully convolutional neural networks for volumetric medical image segmentation
Milletari, F.; Navab, N.; and Ahmadi, S.-A. 2016 · 2016
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Convolutional neural networks for medical image analysis: Full training or fine tuning?
Tajbakhsh, N.; Shin, J. Y.; Gurudu, S. R.; Hurst, R. T.; Kendall, C. B.; Gotway, M. B.; and Liang, J. 2016 · 2016
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Temporal segment networks: Towards good practices for deep action recognition
Wang, L.; Xiong, Y.; Wang, Z.; Qiao, Y.; Lin, D.; Tang, X.; and Van Gool, L. 2016 · 2016
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Designing neural network architectures using reinforcement learning
Baker, B.; Gupta, O.; Naik, N.; and Raskar, R. 2017 · 2017
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Quo vadis, action recognition? a new model and the kinetics dataset
Carreira, J.; and Zisserman, A. 2017 · 2017
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Xception: Deep learning with depthwise separable convolutions
Chollet, F. 2017 · 2017
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The” Something Something” Video Database for Learning and Evaluating Visual Common Sense
Goyal, R.; Kahou, S. E.; Michalski, V.; Materzynska, J.; Westphal, S.; Kim, H.; Haenel, V.; Fruend, I.; Yianilos, P.; Mueller-Freitag, M.; et al. 2017 · 2017
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3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
Liu, S.; Xu, D.; Zhou, S. K.; Mertelmeier, T.; Wicklein, J.; Jerebko, A.; Grbic, S.; Pauly, O.; Cai, W.; and Comaniciu, D. 2017 · 2017
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Learning spatio-temporal representation with pseudo-3d residual networks
Qiu, Z.; Yao, T.; and Mei, T. 2017 · 2017
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Genetic cnn
Xie, L.; and Yuille, A. 2017 · 2017
Cited alongside, same era.
A fixed-point model for pancreas segmentation in abdominal CT scans
Zhou, Y.; Xie, L.; Shen, W.; Wang, Y.; Fishman, E. K.; and Yuille, A. L. 2017 · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Zoph, B.; and Le, Q. V. 2017 · 2017
Cited alongside, same era.
Understanding and simplifying one-shot architecture search
Bender, G.; Kindermans, P.-J.; Zoph, B.; Vasudevan, V.; and Le, Q. 2018 · 2018
Cited alongside, same era.
SMASH: one-shot model architecture search through hypernetworks
Brock, A.; Lim, T.; Ritchie, J. M.; and Weston, N. 2018 · 2018
Cited alongside, same era.
Parallel separable 3D convolution for video and volumetric data understanding
Gonda, F.; Wei, D.; Parag, T.; and Pfister, H. 2018 · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Zoph, B.; Vasudevan, V.; Shlens, J.; and Le, Q. V. 2018 · 2018
Later among the works it cites.
Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation
Chen, X.; Xie, L.; Wu, J.; and Tian, Q. 2019 · 2019
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Searching for a robust neural architecture in four gpu hours
Dong, X.; and Yang, Y. 2019 · 2019
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Nas-fpn: Learning scalable feature pyramid architecture for object detection
Ghiasi, G.; Lin, T.-Y.; and Le, Q. V. 2019 · 2019
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Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples
Li, Y.; Zhu, Z.; Zhou, Y.; Xia, Y.; Shen, W.; Fishman, E. K.; and Yuille, A. L. 2019 · 2019
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Morphnet: Fast & simple resource-constrained structure learning of deep networks
Gordon, A.; Eban, E.; Nachum, O.; Chen, B.; Wu, H.; Yang, T.-J.; and Choi, E. 2018 · 2018
Cited alongside, same era.
3d anisotropic hybrid network: Transferring convolutional features from 2d images to 3d anisotropic volumes
Liu, S.; Xu, D.; Zhou, S. K.; Pauly, O.; Grbic, S.; Mertelmeier, T.; Wicklein, J.; Jerebko, A.; Cai, W.; and Comaniciu, D. 2018 · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Pham, H.; Guan, M. Y.; Zoph, B.; Le, Q. V.; and Dean, J. 2018 · 2018
Cited alongside, same era.
A closer look at spatiotemporal convolutions for action recognition
Tran, D.; Wang, H.; Torresani, L.; Ray, J.; LeCun, Y.; and Paluri, M. 2018 · 2018
Cited alongside, same era.
Non-local neural networks
Wang, X.; Girshick, R.; Gupta, A.; and He, K. 2018 · 2018
Cited alongside, same era.
Videos as space-time region graphs
Wang, X.; and Gupta, A. 2018 · 2018
Cited alongside, same era.
Tsm: Temporal shift module for efficient video understanding
Lin, J.; Gan, C.; and Han, S. 2019 · 2019
Later among the works it cites.
Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation
Liu, C.; Chen, L.-C.; Schroff, F.; Adam, H.; Hua, W.; Yuille, A. L.; and Fei-Fei, L. 2019 · 2019
Later among the works it cites.
Darts: Differentiable architecture search
Liu, H.; Simonyan, K.; and Yang, Y. 2019 · 2019
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Grouped Spatial-Temporal Aggretation for Efficient Action Recognition
Luo, C.; and Yuille, A. 2019 · 2019
Later among the works it cites.
Regularized evolution for image classifier architecture search
Real, E.; Aggarwal, A.; Huang, Y.; and Le, Q. V. 2019 · 2019
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Mixconv: Mixed depthwise convolutional kernels
Tan, M.; and Le, Q. V. 2019 · 2019
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Video classification with channel-separated convolutional networks
Tran, D.; Wang, H.; Torresani, L.; and Feiszli, M. 2019 · 2019
Later among the works it cites.
V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation
Zhu, Z.; Liu, C.; Yang, D.; Yuille, A.; and Xu, D. 2019 · 2019
Later among the works it cites.
Neural Architecture Search for Lightweight Non-Local Networks
Li, Y.; Jin, X.; Mei, J.; Lian, X.; Yang, L.; Xie, C.; Yu, Q.; Zhou, Y.; Bai, S.; and Yuille, A. L. 2020 · 2020
Closest in time.
AtomNAS: Fine-Grained End-to-End Neural Architecture Search
Mei, J.; Li, Y.; Lian, X.; Jin, X.; Yang, L.; Yuille, A.; and Yang, J. 2020 · 2020
Closest in time.
Pc-darts: Partial channel connections for memory-efficient differentiable architecture search
Xu, Y.; Xie, L.; Zhang, X.; Chen, X.; Qi, G.-J.; Tian, Q.; and Xiong, H. 2020 · 2020
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NAS evaluation is frustratingly hard
Yang, A.; Esperança, P. M.; and Carlucci, F. M. 2020 · 2020
Closest in time.