The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Guiding the Long-Short Term Memory Model for Image Caption Generation
Open accessAbstract
In this work we focus on the problem of image caption generation. We propose an extension of the long short term memory (LSTM) model, which we coin gLSTM for short. In particular, we add semantic information extracted from the image as extra input to each unit of the LSTM block, with the aim of guiding the model towards solutions that are more tightly coupled to the image content. Additionally, we explore different length normalization strategies for beam search to avoid bias towards short sentences. On various benchmark datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on par with or better than the current state-of-the-art.
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Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
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Microsoft COCO: Common Objects in Context
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
BLEU: a method for automatic evaluation of machine translation
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Sequence to Sequence Learning with Neural Networks
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Neural Machine Translation by Jointly Learning to Align and Translate
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
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Cited by
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879
“[21, 59, 61, 144, 150, 152] have performed experiments using the dataset.”
BackgroundGenerating Visual Explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., & Darrell, T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_1 · Cited by 534
What Value Do Explicit High Level Concepts Have in Vision to Language Problems?
Wu, Q., Shen, C., Liu, L., Dick, A., & Van Den Hengel, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.29 · Cited by 452
From Show to Tell: A Survey on Deep Learning-Based Image Captioning
Stefanini, M., Cornia, M., Baraldi, L., Cascianelli, S., Fiameni, G., & Cucchiara, R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3148210 · Cited by 373
Metadata
- Authors
- Jia, X., Gavves, E., Fernando, B., & Tuytelaars, T.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2407-2415
- OpenAlex
- W2220981600
- Cited by
- 426
- References
- 41
- FWCI
- 14.96
- Type
- conference paper
- Language
- en
- Open access
- green
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- Jia, X., Gavves, E., Fernando, B., & Tuytelaars, T. (2015). Guiding the Long-Short Term Memory Model for Image Caption Generation. 2015 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2015.277
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