The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
BabyTalk: Understanding and Generating Simple Image Descriptions
Abstract
We present a system to automatically generate natural language descriptions from images. This system consists of two parts. The first part, content planning, smooths the output of computer vision-based detection and recognition algorithms with statistics mined from large pools of visually descriptive text to determine the best content words to use to describe an image. The second step, surface realization, chooses words to construct natural language sentences based on the predicted content and general statistics from natural language. We present multiple approaches for the surface realization step and evaluate each using automatic measures of similarity to human generated reference descriptions. We also collect forced choice human evaluations between descriptions from the proposed generation system and descriptions from competing approaches. The proposed system is very effective at producing relevant sentences for images. It also generates descriptions that are notably more true to the specific image content than previous work.
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Cites
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., & Forsyth, D. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206772 · Cited by 1,917
Perceiving, remembering, and communicating structure in events.
Zacks, J. M., Tversky, B., & Iyer, G. (2001) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.130.1.29 · Cited by 640
Cited by
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
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
SPICE: Semantic Propositional Image Caption Evaluation
Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_24 · Cited by 1,986
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., & Hockenmaier, J. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3994 · Cited by 1,314
Metadata
- Authors
- Kulkarni, G., Premraj, V., Ordonez, V., Dhar, S., Li, S., Choi, Y., Berg, A. C., & Berg, T. L.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-12
- Volume
- 35
- Issue
- 12
- Pages
- 2891-2903
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1969616664
- Cited by
- 839
- References
- 4
- FWCI
- 42.75
- Type
- journal article
- Language
- en
- Open access
- closed
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- Kulkarni, G., Premraj, V., Ordonez, V., Dhar, S., Li, S., Choi, Y., Berg, A. C., & Berg, T. L. (2013). BabyTalk: Understanding and Generating Simple Image Descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2012.162
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