The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Areas of Attention for Image Captioning
Abstract
We propose “Areas of Attention”, a novel attentionbased model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attentionbased approaches that associate image regions only to the RNN state, our method allows a direct association between caption words and image regions. During training these associations are inferred from image-level captions, akin to weakly-supervised object detector training. These associations help to improve captioning by localizing the corresponding regions during testing. We also propose and compare different ways of generating attention areas: CNN activation grids, object proposals, and spatial transformers nets applied in a convolutional fashion. Spatial transformers give the best results. They allow for image specific attention areas, and can be trained jointly with the rest of the network. Our attention mechanism and spatial transformer attention areas together yield state-of-the-art results on the MSCOCO dataset.
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Cites
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Show and tell: A neural image caption generator
Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318
Deep visual-semantic alignments for generating image descriptions
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Cited by
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering
Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S., & Zhang, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00636 · Cited by 4,948
Meshed-Memory Transformer for Image Captioning
Cornia, M., Stefanini, M., Baraldi, L., & Cucchiara, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01059 · Cited by 1,259
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
“[112] proposed an area-based attention mechanism for image captioning.”
BackgroundMethodResultInfluentialGeneralizing Face Forgery Detection with High-frequency Features
Luo, Y., Zhang, Y., Yan, J., & Liu, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01605 · Cited by 609
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
- Pedersoli, M., Lucas, T., Schmid, C., & Verbeek, J.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1251-1259
- OpenAlex
- W2560645892
- Cited by
- 208
- References
- 10
- FWCI
- 7.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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