Exploiting Multiple Sequence Lengths in Fast End to End Training for Image Captioning

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. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255

2009

Earlier work this paper cites.

R. Socher and L. Fei-Fei, “Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition . IEEE, 2010, pp. 966–973

2010

Earlier work this paper cites.

B. Z. Yao, X. Yang, L. Lin, M. W. Lee, and S.-C. Zhu, “I2t: Image parsing to text description,” Proceedings of the IEEE , vol. 98, no. 8, pp. 1485–1508, 2010

2010

Earlier work this paper cites.

M. Mitchell et al. , “Midge: Generating image descriptions from computer vision detections,” in Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics , 2012, pp. 747–756

2012

Earlier work this paper cites.

G. Kulkarni et al. , “Babytalk: Understanding and generating simple image descriptions,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 12, pp. 2891–2903, 2013

2013

Earlier work this paper cites.

K. Cho et al. , “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078 , 2014

Original

2014

Earlier work this paper cites.

D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014

Original

2014

Earlier work this paper cites.

T.-Y. Lin et al. , “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755

2014

Earlier work this paper cites.

O. Vinyals, A. Toshev, S. Bengio, and D. Erhan, “Show and tell: A neural image caption generator,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3156–3164

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,” arXiv preprint arXiv:1506.01497 , 2015

Original

2015

Earlier work this paper cites.

M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025 , 2015

Original

2015

Earlier work this paper cites.

A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3128–3137

2015

Earlier work this paper cites.

P. Anderson, B. Fernando, M. Johnson, and S. Gould, “Guided open vocabulary image captioning with constrained beam search,” arXiv preprint arXiv:1612.00576 , 2016

Original

2016

Earlier work this paper cites.

A. Vaswani et al. , “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008

2017

Earlier work this paper cites.

S. J. Rennie, E. Marcheret, Y. Mroueh, J. Ross, and V. Goel, “Self-critical sequence training for image captioning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 7008–7024

2017

Earlier work this paper cites.

P. Anderson et al. , “Bottom-up and top-down attention for image captioning and visual question answering,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6077–6086

2018

Earlier work this paper cites.

J.-H. Kim, J. Jun, and B.-T. Zhang, “Bilinear attention networks,” arXiv preprint arXiv:1805.07932 , 2018

Original

2018

Earlier work this paper cites.

T. Yao, Y. Pan, Y. Li, and T. Mei, “Exploring visual relationship for image captioning,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 684–699

2018

Earlier work this paper cites.

S. Herdade, A. Kappeler, K. Boakye, and J. Soares, “Image captioning: Transforming objects into words,” arXiv preprint arXiv:1906.05963 , 2019

Original

2019

Cited alongside, same era.

Q. Guo, X. Qiu, P. Liu, Y. Shao, X. Xue, and Z. Zhang, “Star-transformer,” arXiv preprint arXiv:1902.09113 , 2019

Original

2019

Cited alongside, same era.

J. Hao, X. Wang, B. Yang, L. Wang, J. Zhang, and Z. Tu, “Modeling recurrence for transformer,” arXiv preprint arXiv:1904.03092 , 2019

Original

2019

Cited alongside, same era.

L. Huang, W. Wang, J. Chen, and X.-Y. Wei, “Attention on attention for image captioning,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 4634–4643

2019

Cited alongside, same era.

S. Sukhbaatar, E. Grave, G. Lample, H. Jegou, and A. Joulin, “Augmenting self-attention with persistent memory,” arXiv preprint arXiv:1907.01470 , 2019

Y. Tay, D. Bahri, D. Metzler, D.-C. Juan, Z. Zhao, and C. Zheng, “Synthesizer: Rethinking self-attention for transformer models,” in International conference on machine learning . PMLR, 2021, pp. 10 183–10 192

2021

Later among the works it cites.

J. Lee-Thorp, J. Ainslie, I. Eckstein, and S. Ontanon, “Fnet: Mixing tokens with fourier transforms,” arXiv preprint arXiv:2105.03824 , 2021

Original

2021

Later among the works it cites.

I. O. Tolstikhin et al. , “Mlp-mixer: An all-mlp architecture for vision,” Advances in neural information processing systems , vol. 34, pp. 24 261–24 272, 2021

2021

Later among the works it cites.

J. Ji et al. , “Improving image captioning by leveraging intra-and inter-layer global representation in transformer network,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 2, 2021, pp. 1655–1663

2021

Later among the works it cites.