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We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Modeling context between objects for referring expression understanding
V. K. Nagaraja, V. I. Morariu, and L. S. Davis · 2016
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Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh · 2017
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Learning to reason: End-to-end module networks for visual question answering
R. Hu, J. Andreas, M. Rohrbach, T. Darrell, and K. Saenko · 2017
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Building machines that learn and think like people
B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman · 2017
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A simple neural network module for relational reasoning
A. Santoro, D. Raposo, D. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap · 2017
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Scene graph generation by iterative message passing
D. Xu, Y. Zhu, C. B. Choy, and L. Fei-Fei · 2017
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A joint speaker-listener-reinforcer model for referring expressions
L. Yu, H. Tan, M. Bansal, and T. L. Berg · 2017
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Bottom-up and top-down attention for image captioning and visual question answering
P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang · 2018
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Object level visual reasoning in videos
F. Baradel, N. Neverova, C. Wolf, J. Mille, and G. Mori · 2018
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Object-based reasoning in VQA
M. Desta, L. Chen, and T. Kornuta · 2018
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Compositional attention networks for machine reasoning
D. A. Hudson and C. D. Manning · 2018
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Visual reasoning by progressive module networks
S. W. Kim, M. Tapaswi, and S. Fidler · 2018
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2018
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Murel: Multimodal relational reasoning for visual question answering
R. Cadene, H. Ben-Younes, M. Cord, and N. Thome · 2019
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Language-conditioned graph networks for relational reasoning
R. Hu, A. Rohrbach, T. Darrell, and K. Saenko · 2019
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Multi-grained attention with object-level grounding for visual question answering
P. Huang, J. Huang, Y. Guo, M. Qiao, and Y. Zhu · 2019
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Relation-aware graph attention network for visual question answering
L. Li, Z. Gan, Y. Cheng, and J. Liu · 2019
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Explainable and explicit visual reasoning over scene graphs
J. Shi, H. Zhang, and J. Li · 2019
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Learning conditioned graph structures for interpretable visual question answering
W. Norcliffe-Brown, S. Vafeias, and S. Parisot · 2018
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Recurrent relational networks
R. Palm, U. Paquet, and O. Winther · 2018
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Film: Visual reasoning with a general conditioning layer
E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville · 2018
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Learning by abstraction: The neural state machine
D. Hudson and C. D. Manning
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Gqa: A new dataset for real-world visual reasoning and compositional qa
D. A. Hudson and C. D. Manning
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
J. Johnson, B. Hariharan, L. van der Maaten, L. Fei-Fei, C. Lawrence Zitnick, and R. Girshick
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Answer them all! toward universal visual question answering models
R. Shrestha, K. Kafle, and C. Kanan · 2019
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Dynamic graph attention for referring expression comprehension
S. Yang, G. Li, and Y. Yu · 2019
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What can neural networks reason about?
K. Xu, J. Li, M. Zhang, S. S. Du, K.-i. Kawarabayashi, and S. Jegelka · 2020
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