The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Attention on Attention for Image Captioning
Open accessAbstract
Attention mechanisms are widely used in current encoder/decoder frameworks of image captioning, where a weighted average on encoded vectors is generated at each time step to guide the caption decoding process. However, the decoder has little idea of whether or how well the attended vector and the given attention query are related, which could make the decoder give misled results. In this paper, we propose an Attention on Attention (AoA) module, which extends the conventional attention mechanisms to determine the relevance between attention results and queries. AoA first generates an information vector and an attention gate using the attention result and the current context, then adds another attention by applying element-wise multiplication to them and finally obtains the attended information, the expected useful knowledge. We apply AoA to both the encoder and the decoder of our image captioning model, which we name as AoA Network (AoANet). Experiments show that AoANet outperforms all previously published methods and achieves a new state-of-the-art performance of 129.8 CIDEr-D score on MS COCO Karpathy offline test split and 129.6 CIDEr-D (C40) score on the official online testing server. Code is available at https://github.com/husthuaan/AoANet.
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“DropToken [174] reduces the training complexity via random dropping a portion of the video and audio tokens from input sequence during training.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [174]
Citations
Cites
Deep Residual Learning for Image Recognition
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Adam: A Method for Stochastic Optimization
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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
Microsoft COCO: Common Objects in Context
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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BLEU: a method for automatic evaluation of machine translation
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Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
Li, X., Yin, X. P., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Li, D., Wei, F., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_8 · Cited by 1,791
“2 AoANet [11] 37.”
ResultMeshed-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
“Noticeably, when using four models our approach achieves the best performance according to all metrics, with an increase of 2.5 CIDEr points with respect to the current state of the art [14].”
BackgroundMethodInfluentialMultimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072
“graph [228], video to caption [229], [230], [231], [232], image to caption [233], [234], [235], [236], [237], text to speech [238], text to image [205], [239], text to shape [240], RGB to 3D human”
BackgroundThe Rise and Potential of Large Language Model Based Agents: A Survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2023) · arXiv · doi:10.48550/arxiv.2309.07864 · Cited by 972
Self-citationThe rise and potential of large language model based agents: a survey
Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4222-0 · Cited by 970
“To help the agent understand the information contained within images, a straightforward approach is to generate corresponding text descriptions for image inputs, known as image captioning [203–207].”
Self-citation
Metadata
- Authors
- Huang, L., Wang, W., Chen, J., & Wei, X.-Y.
- Affiliations
- Peking University (CN)
- Macau University of Science and Technology (MO)
- Peng Cheng Laboratory (CN)
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 4633-4642
- OpenAlex
- W2986670728
- Semantic Scholar
- 4c163d4942117179d3e97182e1b280027d7d60a9
- Cited by
- 1,092
- References
- 54
- Citations by year
- 2026: 52 · 2025: 142 · 2024: 197 · 2023: 228 · 2022: 212 · 2021: 188 · 2020: 70 · 2019: 2
- FWCI
- 38.47
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (U-1613209, 61972217, 61872256)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- image captioning, attention mechanism, attention gate, encoder-decoder architecture, MS COCO
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