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Visual Question answering is a challenging problem requiring a combination of concepts from Computer Vision and Natural Language Processing.
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst · 2013
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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel · 2015
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Multimodal compact bilinear pooling for visual question answering and visual grounding
Akira Fukui, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach · 2016
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Hierarchical question-image co-attention for visual question answering
Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2016
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Graph-structured representations for visual question answering
Damien Teney, Lingqiao Liu, and Anton van den Hengel · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Learning shape correspondence with anisotropic convolutional neural networks
Davide Boscaini, Jonathan Masci, Emanuele Rodolà, and Michael Bronstein · 2016
Cited alongside, same era.
Visual question answering: A survey of methods and datasets
Qi Wu, Damien Teney, Peng Wang, Chunhua Shen, Anthony Dick, and Anton van den Hengel · 2017
Cited alongside, same era.
Tips and tricks for visual question answering: Learnings from the 2017 challenge
Damien Teney, Peter Anderson, Xiaodong He, and Anton van den Hengel · 2017
Cited alongside, same era.
High-order attention models for visual question answering
Idan Schwartz, Alexander G. Schwing, and Tamir Hazan · 2017
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Multimodal learning and reasoning for visual question answering
Ilija Ilievski and Jiashi Feng · 2017
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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 question answering: Datasets, algorithms, and future challenges
Kushal Kafle and Christopher Kanan · 2017
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Learning to count objects in natural images for visual question answering
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Scene graph generation by iterative message passing
Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M. Bronstein · 2017
Cited alongside, same era.
Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang · 2017
Cited alongside, same era.
Yan Zhang, Jonathon S. Hare, and Adam Prügel-Bennett · 2018
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Interpretable counting for visual question answering
Alexander Trott, Caiming Xiong, and Richard Socher · 2018
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