The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding
Abstract
We address the problem of dense visual-semantic embedding that maps not only full sentences and whole images but also phrases within sentences and salient regions within images into a multimodal embedding space. Such dense embeddings, when applied to the task of image captioning, enable us to produce several region-oriented and detailed phrases rather than just an overview sentence to describe an image. Specifically, we present a hierarchical structured recurrent neural network (RNN), namely Hierarchical Multimodal LSTM (HM-LSTM). Compared with chain structured RNN, our proposed model exploits the hierarchical relations between sentences and phrases, and between whole images and image regions, to jointly establish their representations. Without the need of any supervised labels, our proposed model automatically learns the fine-grained correspondences between phrases and image regions towards the dense embedding. Extensive experiments on several datasets validate the efficacy of our method, which compares favorably with the state-of-the-art methods.
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ImageNet: A large-scale hierarchical image database
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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
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
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
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Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models
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Stacked Cross Attention for Image-Text Matching
Lee, K.-H., Chen, X., Hua, G., Hu, H., & He, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01225-0_13 · Cited by 1,402
Self-citationIMRAM: Iterative Matching With Recurrent Attention Memory for Cross-Modal Image-Text Retrieval
Chen, H., Ding, G., Liu, X., Lin, Z., Liu, J., & Han, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01267 · Cited by 429
CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrieval
Wang, Z., Liu, X., Li, H., Sheng, L., Yan, J., Wang, X., & Shao, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00586 · Cited by 359
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
Self-citationGuo, W., Huang, H., Kong, X., & He, R. (2019) · Proceedings of the 27th ACM International Conference on Multimedia · doi:10.1145/3343031.3351053 · Cited by 36
Metadata
- Authors
- Niu, Z., Zhou, M., Wang, L., Gao, X., & Hua, G.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 1899-1907
- OpenAlex
- W2778940641
- Cited by
- 161
- References
- 8
- FWCI
- 6.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Niu, Z., Zhou, M., Wang, L., Gao, X., & Hua, G. (2017). Hierarchical Multimodal LSTM for Dense Visual-Semantic Embedding. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.208
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