The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Task-Adaptive Attention for Image Captioning
Abstract
Attention mechanisms are now widely used in image captioning models. However, most attention models only focus on visual features. When generating syntax related words, little visual information is needed. In this case, these attention models could mislead the word generation. In this paper, we propose Task-Adaptive Attention module for image captioning, which can alleviate this misleading problem and learn implicit non-visual clues which can be helpful for the generation of non-visual words. We further introduce a diversity regularization to enhance the expression ability of the Task-Adaptive Attention module. Extensive experiments on the MSCOCO captioning dataset demonstrate that by plugging our Task-Adaptive Attention module into a vanilla Transformer-based image captioning model, performance improvement can be achieved.
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Cites
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
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
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107
Self-citationBottom-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
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
Self-Critical Sequence Training for Image Captioning
Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.131 · Cited by 2,112
Sequence Level Training with Recurrent Neural Networks
Ranzato, M., Chopra, S., Auli, M., & Zaremba, W. (2015) · arXiv · doi:10.48550/arxiv.1511.06732 · Cited by 1,426
Boosting Image Captioning with Attributes
Yao, T., Pan, Y., Li, Y., Qiu, Z., & Mei, T. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.524 · Cited by 733
Video Captioning With Attention-Based LSTM and Semantic Consistency
Gao, L., Guo, Z., Zhang, H., Xu, X., & Shen, H. T. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2729019 · Cited by 637
Deep Multi-View Enhancement Hashing for Image Retrieval
Yan, C., Gong, B., Wei, Y., & Gao, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2975798 · Cited by 504
Self-citation
Cited by
Kılıç, B., & Aydın, S. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09579-2 · Cited by 112
Metadata
- Authors
- Yan, C., Hao, Y., Li, L., Yin, J., Liu, A., Mao, Z., Chen, Z., & Gao, X.
- Venue
- IEEE Transactions on Circuits and Systems for Video Technology
- Published
- 2022-01
- Volume
- 32
- Issue
- 1
- Pages
- 43-51
- ISSN
- 1051-8215 (ISSN-L)
- 1558-2205
- OpenAlex
- W3136792391
- Cited by
- 367
- References
- 28
- FWCI
- 20.72
- Type
- journal article
- Language
- en
- Open access
- closed
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