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Visual attention mechanisms are a key component of neural network models for computer vision.
Maximum likelihood from incomplete data via the EM algorithm
A. P. Dempster, N. M. Laird, and D. B. Rubin · 1977
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Estimating the Dimension of a Model
Gideon Schwarz · 1978
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Possible generalization of Boltzmann-Gibbs statistics
Constantino Tsallis · 1988
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Stochastic Complexity in Statistical Inquiry Theory
Jorma Rissanen · 1989
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The EM algorithm and extensions
Geoffrey J. McLachlan and T. Krishnan · 1997
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Finite mixture models
Geoffrey J. McLachlan and David Peel · 2000
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The Dynamic Representation of Scenes
Ronald A Rensink · 2000
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Unsupervised learning of finite mixture models
Mario A. T. Figueiredo and Anil K. Jain · 2002
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning · 2014
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Neural machine translation by jointly learning to align and translate
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Action recognition using visual attention
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Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?
Abhishek Das, Harsh Agrawal, C. Lawrence Zitnick, Devi Parikh, and Dhruv Batra · 2016
Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Progressive attention guided recurrent network for salient object detection
Xiaoning Zhang, Tiantian Wang, Jinqing Qi, Huchuan Lu, and Gang Wang · 2018
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Attention is not not Explanation
Sarah Wiegreffe and Yuval Pinter · 2019
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Deep modular co-attention networks for visual question answering
Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian · 2019
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Learning with fenchel-young losses
Mathieu Blondel, André FT Martins, and Vlad Niculae · 2020
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Attention in natural language processing
Andrea Galassi, Marco Lippi, and Paolo Torroni · 2020
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Em algorithms for weighted-data clustering with application to audio-visual scene analysis
Israel D. Gebru, Xavier Alameda-Pineda, F. Forbes, and R. Horaud · 2016
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, A. David Shamma, S. Michael Bernstein, and Fei-Fei Li · 2017
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In defense of grid features for visual question answering
Huaizu Jiang, I. Misra, Marcus Rohrbach, E. Learned-Miller, and Xinlei Chen · 2020
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Sparse and Continuous Attention Mechanisms
André Martins, António Farinhas, Marcos Treviso, Vlad Niculae, Pedro Aguiar, and Mario Figueiredo · 2020
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Sparse and structured visual attention, 2020
Pedro Henrique Martins, Vlad Niculae, Zita Marinho, and André Martins · 2020
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