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Modeling visual question answering(VQA) through scene graphs can significantly improve the reasoning accuracy and interpretability.
“GloVe: Global vectors for word representation,”
J. P., R. Socher, and C. Manning, · 2014
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“Hierarchical question-image co-attention for visual question answering,”
J. Lu, J. Yang, D. Batra, and D. Parikh, · 2016
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“Transition-based chinese semantic dependency graph parsing,”
Y. Wang, J. Guo, W. Che, and T. Liu, · 2016
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“Gated graph sequence neural networks,”
Y. Li, D. Tarlow, M. Brockschmidt, and R.S. Zemel, · 2016
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“Visual genome: Connecting language and vision using crowdsourced dense image annotations,”
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L. Li, D.A. Shamma, M.S. Bernstein, and Li Fei-Fei, · 2017
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“Differential attention for visual question answering,”
Badri N. Patro and Vinay P. Namboodiri, · 2018
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“Compositional attention networks for machine reasoning,”
D.A. Hudson and C.D. Manning, · 2018
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“A neural transition-based approach for semantic dependency graph parsing,”
Y. Wang, W. Che, J. Guo, and T. Liu, · 2018
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“Zoom-net: Mining deep feature interactions for visual relationship recognition,”
G. Yin, L. Sheng, B. Liu, N. Yu, X. Wang, J. Shao, and Chen C., · 2018
Cited alongside, same era.
“An empirical study on leveraging scene graphs for visual question answering,”
C. Zhang, W. Chao, and D. Xuan, · 2019
Cited alongside, same era.
“Learning by abstraction: The neural state machine,”
D.A. Hudson and C.D. Manning, · 2019
Cited alongside, same era.
“Relation-aware graph attention network for visual question answering,”
L. Li, Z. Gan, Y. Cheng, and J. Liu, · 2019
Cited alongside, same era.
“Spatial-aware graph relation network for large-scale object detection,”
H. Xu, C. Jiang, X. Liang, and Z. Li, · 2019
Cited alongside, same era.
“BERT: pre-training of deep bidirectional transformers for language understanding,”
J. Devlin, M. Chang, K. Lee, and K. Toutanova, · 2019
“Prior visual relationship reasoning for visual question answering,”
Z. Yang, Z. Qin, J. Yu, and T. Wan, · 2020
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“Sgg codebase in pytorch,” 2020
K. Tang, · 2020
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“Unbiased scene graph generation from biased training,”
K. Tang, Y. Niu, J. Huang, J. Shi, and H. Zhang, · 2020
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“Unicoder-vl: A universal encoder for vision and language by cross-modal pre-training,”
G. Li, N. Duan, Y. Fang, Ming G, and Daxin J, · 2020
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“Understanding the role of scene graphs in visual question answering,”
V. Damodaran, S. Chakravarthy, A. Kumar, A. Umapathy, T. Mitamura, Y. Nakashima, N. Garcia, and C. Chu, · 2021
Later among the works it cites.
“Interpretable visual question answering by reasoning on dependency trees,”
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Cited alongside, same era.
“GQA: A new dataset for real-world visual reasoning and compositional question answering,”
D.A. Hudson and C.D. Manning, · 2019
Cited alongside, same era.
“Scene graph reasoning for visual question answering,”
M. Hildebrandt, H. Li, R. Koner, V. Tresp, and S. Günnemann, · 2020
Cited alongside, same era.
“From strings to things: Knowledge-enabled vqa model that can read and reason,”
Aj. Singh, An. Mishra, Sh. Shekhar, and An. Chakraborty,
Cited in the paper.
“Graph attention networks,”
P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio,
Cited in the paper.
Q. Cao, X. Liang, B. Li, and L. Lin, · 2021
Later among the works it cites.
“Pre-trained models: Past, present and future,”
X. Han, Z. Zhang, N. Ding, J. Wen, J. Yuan, W. Zhao, and J. Zhu, · 2021
Later among the works it cites.
“Graphvqa: Language-guided graph neural networks for scene graph question answering,”
W. Liange, Y. Jiang, and Z. Liu, · 2021
Later among the works it cites.