Fetching the paper…
Reading the bibliography…
Current methods of Visual Question Answering perform well on the answers with an amount of training data but have limited accuracy on the novel ones with few examples.
H. Robbins and S. Monro, “A stochastic approximation method,” The annals of mathematical statistics , pp. 400–407, 1951
1951
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “Sun database: Large-scale scene recognition from abbey to zoo,” in 2010 IEEE computer society conference on computer vision and pattern recognition . IEEE, 2010, pp. 3485–3492
2010
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The caltech-ucsd birds-200-2011 dataset,” 2011
2011
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755
2014
Earlier work this paper cites.
J. Pennington, R. Socher, and C. D. Manning, “Glove: Global vectors for word representation,” in Empirical Methods in Natural Language Processing (EMNLP) , 2014, pp. 1532–1543. [Online]. Available: http://www.aclweb.org/anthology/D14-1162
2014
Earlier work this paper cites.
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh, “Vqa: Visual question answering,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2425–2433
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International journal of computer vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
——, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1026–1034
2015
Earlier work this paper cites.
G. Koch, R. Zemel, and R. Salakhutdinov, “Siamese neural networks for one-shot image recognition,” in ICML deep learning workshop , vol. 2. Lille, 2015
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
Z. Yang, X. He, J. Gao, L. Deng, and A. Smola, “Stacked attention networks for image question answering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 21–29
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” 2016
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” in Advances in neural information processing systems , 2016, pp. 3630–3638
2016
Cited alongside, same era.
T. Salimans and D. P. Kingma, “Weight normalization: A simple reparameterization to accelerate training of deep neural networks,” in Advances in Neural Information Processing Systems , 2016, pp. 901–909
2016
Cited alongside, same era.
A. Das, S. Kottur, K. Gupta, A. Singh, D. Yadav, J. M. Moura, D. Parikh, and D. Batra, “Visual dialog,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 326–335
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
X. Dong, L. Zhu, D. Zhang, Y. Yang, and F. Wu, “Fast parameter adaptation for few-shot image captioning and visual question answering,” in Proceedings of the 26th ACM international conference on Multimedia , 2018, pp. 54–62
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
P. Sharma, N. Ding, S. Goodman, and R. Soricut, “Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2018, pp. 2556–2565
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1492–1500
2017
Cited alongside, same era.
Z. Yu, J. Yu, J. Fan, and D. Tao, “Multi-modal factorized bilinear pooling with co-attention learning for visual question answering,” IEEE International Conference on Computer Vision (ICCV) , pp. 1839–1848, 2017
2017
Cited alongside, same era.
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma et al. , “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” International Journal of Computer Vision , vol. 123, no. 1, pp. 32–73, 2017
2017
Cited alongside, same era.
T. Munkhdalai and H. Yu, “Meta networks,” Proceedings of machine learning research , vol. 70, p. 2554, 2017
2017
Cited alongside, same era.
J. Snell, K. Swersky, and R. Zemel, “Prototypical networks for few-shot learning,” in Advances in neural information processing systems , 2017, pp. 4077–4087
2017
Cited alongside, same era.
2017
Cited alongside, same era.
D. Teney, P. Anderson, X. He, and A. Van Den Hengel, “Tips and tricks for visual question answering: Learnings from the 2017 challenge,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4223–4232
2018
Later among the works it cites.
P. Gao, Z. Jiang, H. You, P. Lu, S. C. Hoi, X. Wang, and H. Li, “Dynamic fusion with intra-and inter-modality attention flow for visual question answering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6639–6648
2019
Later among the works it cites.
Z. Yu, J. Yu, Y. Cui, D. Tao, and Q. Tian, “Deep modular co-attention networks for visual question answering,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6281–6290
2019
Later among the works it cites.
D. Guo, C. Xu, and D. Tao, “Image-question-answer synergistic network for visual dialog,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 434–10 443
2019
Later among the works it cites.
P. Tokmakov, Y.-X. Wang, and M. Hebert, “Learning compositional representations for few-shot recognition,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 6372–6381
2019
Later among the works it cites.
D. Guo, C. Xu, and D. Tao, “Bilinear graph networks for visual question answering,” arXiv , pp. arXiv–1907, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
H. Jiang, I. Misra, M. Rohrbach, E. Learned-Miller, and X. Chen, “In defense of grid features for visual question answering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 10 267–10 276
2020
Later among the works it cites.
D. Huynh and E. Elhamifar, “Fine-grained generalized zero-shot learning via dense attribute-based attention,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4483–4493
2020
Later among the works it cites.