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The state-of-the-art deep neural networks are vulnerable to common corruptions (e.g., input data degradations, distortions, and disturbances caused by weather changes, system error, and processing).
Training with noise is equivalent to tikhonov regularization
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Video quality assessment based on structural distortion measurement
Zhou Wang, Ligang Lu, and Alan C Bovik · 2004
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Visual sensitivity guided bit allocation for video coding
Chih-Wei Tang, Ching-Ho Chen, Ya-Hui Yu, and Chun-Jen Tsai · 2006
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Analysis of packet loss for compressed video: Effect of burst losses and correlation between error frames
Yi J Liang, John G Apostolopoulos, and Bernd Girod · 2008
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Video dehazing with spatial and temporal coherence
Jiawan Zhang, Liang Li, Yi Zhang, Guoqiang Yang, Xiaochun Cao, and Jizhou Sun · 2011
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Temporally x real-time video dehazing
Jin-Hwan Kim, Won-Dong Jang, Yongsup Park, Dong-Hahk Lee, Jae-Young Sim, and Chang-Su Kim · 2012
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Video denoising, deblocking, and enhancement through separable 4-d nonlocal spatiotemporal transforms
Matteo Maggioni, Giacomo Boracchi, Alessandro Foi, and Karen Egiazarian · 2012
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Generalized video deblurring for dynamic scenes
Tae Hyun Kim and Kyoung Mu Lee · 2015
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Two-stream convolutional networks for action recognition
Karen Simonyan and Andrew Zisserman · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Learning spatiotemporal features with 3d convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
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Temporal segment networks: Towards good practices for deep action recognition
Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, and Luc Van Gool · 2016
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Improving the robustness of deep neural networks via stability training
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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Comparing deep neural networks against humans: object recognition when the signal gets weaker
Robert Geirhos, David HJ Janssen, Heiko H Schütt, Jonas Rauber, Matthias Bethge, and Felix A Wichmann · 2017
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The" something something" video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al · 2017
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Dynamic video deblurring using a locally adaptive blur model
Tae Hyun Kim, Seungjun Nah, and Kyoung Mu Lee · 2017
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Video denoising via empirical bayesian estimation of space-time patches
Pablo Arias and Jean-Michel Morel · 2018
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos R Medina Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh · 2018
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What makes a video a video: Analyzing temporal information in video understanding models and datasets
De-An Huang, Vignesh Ramanathan, Dhruv Mahajan, Lorenzo Torresani, Manohar Paluri, Li Fei-Fei, and Juan Carlos Niebles · 2018
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D3r-net: Dynamic routing residue recurrent network for video rain removal
Certified adversarial robustness with additive noise
Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin · 2019
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Improving robustness without sacrificing accuracy with patch gaussian augmentation
Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, and Ekin D Cubuk · 2019
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Benchmarking robustness in object detection: Autonomous driving when winter is coming
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S Ecker, Matthias Bethge, and Wieland Brendel · 2019
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Do image classifiers generalize across time?
Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, and Ludwig Schmidt · 2019
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X3d: Expanding architectures for efficient video recognition
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Jiaying Liu, Wenhan Yang, Shuai Yang, and Zongming Guo · 2018
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Erase or fill? deep joint recurrent rain removal and reconstruction in videos
Jiaying Liu, Wenhan Yang, Shuai Yang, and Zongming Guo · 2018
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Towards deep learning models resistant to adversarial attacks
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A closer look at spatiotemporal convolutions for action recognition
Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri · 2018
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Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification
Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy · 2018
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More is less: Learning efficient video representations by big-little network and depthwise temporal aggregation
Quanfu Fan, Chun-Fu (Richard) Chen, Hilde Kuehne, Marco Pistoia, and David Cox · 2019
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Slowfast networks for video recognition
Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He · 2019
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Benchmarking the robustness of semantic segmentation models
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A simple way to make neural networks robust against diverse image corruptions
Evgenia Rusak, Lukas Schott, Roland S Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, and Wieland Brendel · 2020
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Measuring robustness to natural distribution shifts in image classification
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2020
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Is space-time attention all you need for video understanding?
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
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“everyone wants to do the model work, not the data work”: Data cascades in high-stakes ai
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When human pose estimation meets robustness: Adversarial algorithms and benchmarks
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