The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Adversarial Representation Learning for Text-to-Image Matching
Open accessAbstract
For many computer vision applications such as image captioning, visual question answering, and person search, learning discriminative feature representations at both image and text level is an essential yet challenging problem. Its challenges originate from the large word variance in the text domain as well as the difficulty of accurately measuring the distance between the features of the two modalities. Most prior work focuses on the latter challenge, by introducing loss functions that help the network learn better feature representations but fail to account for the complexity of the textual input. With that in mind, we introduce TIMAM: a Text-Image Modality Adversarial Matching approach that learns modality-invariant feature representations using adversarial and cross-modal matching objectives. In addition, we demonstrate that BERT, a publicly-available language model that extracts word embeddings, can successfully be applied in the text-to-image matching domain. The proposed approach achieves state-of-the-art cross-modal matching performance on four widely-used publicly-available datasets resulting in absolute improvements ranging from 2% to 5% in terms of rank-1 accuracy.
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“[32] demonstrated that a joint representation can be learned from facial and voice information and introduced a curriculum learning strategy [3, 40, 41] to perform hard negative mining during training.”
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The multi-modal fusion in visual question answering: a review of attention mechanisms
Lu, S., Liu, M., Yin, L., Yin, Z., Liu, X., & Zheng, W. (2023) · PeerJ Computer Science · doi:10.7717/peerj-cs.1400 · Cited by 477
Graph Structured Network for Image-Text Matching
Liu, C., Mao, Z., Zhang, T., Xie, H., Wang, B., & Zhang, Y. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01093 · Cited by 285
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Metadata
- Authors
- Sarafianos, N., Xu, X., & Kakadiaris, I.
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 5813-5823
- OpenAlex
- W2971355948
- Cited by
- 250
- References
- 17
- FWCI
- 7.84
- Type
- conference paper
- Language
- en
- Open access
- green
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