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Image Captioning with Semantic Attention
Open accessAbstract
Automatically generating a natural language description of an image has attracted interests recently both because of its importance in practical applications and because it connects two major artificial intelligence fields: computer vision and natural language processing. Existing approaches are either top-down, which start from a gist of an image and convert it into words, or bottom-up, which come up with words describing various aspects of an image and then combine them. In this paper, we propose a new algorithm that combines both approaches through a model of semantic attention. Our algorithm learns to selectively attend to semantic concept proposals and fuse them into hidden states and outputs of recurrent neural networks. The selection and fusion form a feedback connecting the top-down and bottom-up computation. We evaluate our algorithm on two public benchmarks: Microsoft COCO and Flickr30K. Experimental results show that our algorithm significantly outperforms the state-of-the-art approaches consistently across different evaluation metrics.
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Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
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
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
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Microsoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614
Going Deeper with Convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461
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Tensor Fusion Network for Multimodal Sentiment Analysis
Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L.-P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1115 · Cited by 2,102
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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
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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
“A general approach of this category is to analyze the visual content of the image first and then generate image captions from the visual content using a language model [70, 152, 155, 156].”
BackgroundMethodInfluentialX-Linear Attention Networks for Image Captioning
Pan, Y., Yao, T., Li, Y., & Mei, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01098 · Cited by 720
End-to-End Dense Video Captioning with Masked Transformer
Zhou, L., Zhou, Y., Corso, J. J., Socher, R., & Xiong, C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00911 · Cited by 605
Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., & Gao, J. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098088 · Cited by 576
Self-citationMultimodal intelligence: Representation learning, information fusion, and applications
Zhang, C., Yang, Z., He, X., & Deng, L. (2020) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2020.2987728 · Cited by 511
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Liu, S., Zhu, Z., Ye, N., Guadarrama, S., & Murphy, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.100 · Cited by 430
On the Automatic Generation of Medical Imaging Reports
Jing, B., Xie, P., & Xing, E. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1240 · Cited by 657
“We first compare our method with several state-of-the-art image captioning models: CNN-RNN [19], LRCN [5], Soft ATT [20], ATT-RK [21].”
BackgroundMethodInfluentialJetley, S., Lord, N. A., Lee, N., & Torr, P. H. S. (2018) · arXiv · doi:10.48550/arxiv.1804.02391 · Cited by 383
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
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
Zhang, Z., Xie, Y., Xing, F., McGough, M., & Yang, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.378 · Cited by 354
Multimodal sentiment analysis with word-level fusion and reinforcement learning
Chen, M., Wang, S., Liang, P. P., Baltrušaitis, T., Zadeh, A., & Morency, L.-P. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3136801 · Cited by 334
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Doran, D., Schulz, S., & Besold, T. R. (2017) · arXiv · doi:10.48550/arxiv.1710.00794 · Cited by 333
Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
Li, C. Y., Liang, X., Hu, Z., & Xing, E. P. (2018) · arXiv · doi:10.48550/arxiv.1805.08298 · Cited by 271
 3: Accelerating attention mechanisms in neural networks with approximation
Ham, T. J., Jung, S. J., Kim, S., Oh, Y. H., Park, Y., Song, Y., Park, J.-H., Lee, S., Park, K., Lee, J. W., et al. (2020) · 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA) · doi:10.1109/hpca47549.2020.00035 · Cited by 242
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Yang, F., Song, X., Xu, F., & Tsui, K.-L. (2019) · IEEE Access · doi:10.1109/access.2019.2912803 · Cited by 234
KAME: Knowledge-based Attention Model for Diagnosis Prediction in Healthcare
Ma, F., You, Q., Xiao, H., Chitta, R., Zhou, J., & Gao, J. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management · doi:10.1145/3269206.3271701 · Cited by 213
Self-citationAutomated rationale generation: a technique for explainable AI and its effects on human perceptions
Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., & Riedl, M. O. (2019) · Proceedings of the 24th International Conference on Intelligent User Interfaces · doi:10.1145/3301275.3302316 · Cited by 210
Readmission prediction using deep learning on electronic health records
Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103256 · Cited by 194
Attention Correctness in Neural Image Captioning
Liu, C., Mao, J., Sha, F., & Yuille, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11197 · Cited by 210
Automated radiology report generation using conditioned transformers
Alfarghaly, O., Khaled, R., Elkorany, A., Helal, M., & Fahmy, A. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100557 · Cited by 183
METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens
Wang, Z., Liu, L., Wang, L., & Zhou, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01112 · Cited by 184
“A broad collection of methods was proposed in the last few years [1,6,24,25,28,32,37,42,45,48] and have achieved great success in advancing the stateof-the-art.”
BackgroundSelf-citationCausal Attention for Vision-Language Tasks
Yang, X., Zhang, H., Qi, G., & Cai, J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00972 · Cited by 175
Vision-Language Pre-Training: Basics, Recent Advances, and Future Trends
Gan, Z., Li, L., Li, C., Wang, L., Liu, Z., & Gao, J. (2022) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000105 · Cited by 155
HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clips
Miech, A., Zhukov, D., Alayrac, J.-B., Tapaswi, M., Laptev, I., & Sivic, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00272 · Cited by 986
“These applications include automatic image and video captioning [20, 36, 60, 61], visual question answering [9, 29, 51, 63], visual content retrieval based on textual queries [32, 56, 63], temporal localization of events in videos using natural language [15, 26] or video summarization with natural language [38].”
BackgroundRegion-Aware Image Captioning via Interaction Learning
Liu, A.-A., Zhai, Y., Xu, N., Nie, W., Li, W., & Zhang, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2021.3107035 · Cited by 128
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Towards Unsupervised Image Captioning With Shared Multimodal Embeddings
Laina, I., Rupprecht, C., & Navab, N. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00751 · Cited by 111
Wang, P.-Y., Chen, C.-T., Su, J.-W., Wang, T.-Y., & Huang, S.-H. (2021) · IEEE Access · doi:10.1109/access.2021.3071306 · Cited by 105
AudioCaps: Generating Captions for Audios in The Wild
Kim, C. D., Kim, B., Lee, H., & Kim, G. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1011 · Cited by 96
Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrieval
Bain, M., Nagrani, A., Varol, G., & Zisserman, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00175 · Cited by 76
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Dong, X., Long, C., Xu, W., & Xiao, C. (2021) · Proceedings of the 29th ACM International Conference on Multimedia · doi:10.1145/3474085.3475439 · Cited by 74
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Wang, H., Feng, J., Zhang, Z., Su, H., Cui, L., He, H., & Liu, L. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.026 · Cited by 68
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Daneshfar, F., Bartani, A., & Lotfi, P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109288 · Cited by 49
Video Question Answering with Spatio-Temporal Reasoning
Jang, Y., Song, Y., Kim, C. D., Yu, Y., Kim, Y., & Kim, G. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01189-x · Cited by 46
Seeing with Humans: Gaze-Assisted Neural Image Captioning
Sugano, Y., & Bulling, A. (2016) · arXiv · doi:10.48550/arxiv.1608.05203 · Cited by 47
Concept-Aware Video Captioning: Describing Videos With Effective Prior Information
Yang, B., Cao, M., & Zou, Y. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3307969 · Cited by 39
Alsharid, M., Cai, Y., Sharma, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102630 · Cited by 25
Multimodal Data Hybrid Fusion and Natural Language Processing for Clinical Prediction Models
Ye, J., Hai, J., Song, J., & Wang, Z. (2023) · doi:10.1101/2023.08.24.23294597 · Cited by 24
Self-citationAutomatic Medical Image Report Generation with Multi-view and Multi-modal Attention Mechanism
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Tevet, G., Habib, G., Shwartz, V., & Berant, J. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1233 · Cited by 12
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Metadata
- Authors
- You, Q., Jin, H., Wang, Z., Fang, C., & Luo, J.
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 4651-4659
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2302086703
- Cited by
- 1,803
- References
- 7
- FWCI
- 74.95
- Type
- conference paper
- Language
- en
- Open access
- green
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