Cross-modal Recipe Retrieval with Hierarchical Transformers and Pretrained Food Image Encoder
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5675-9_36
Reference20 articles.
1. Aguilar, E., et al.: Grab, pay, and eat: semantic food detection for smart restaurants. IEEE Trans. Multim. 20(12), 3266–3275 (2018)
2. Bossard, L., et al.: Food-101–mining discriminative components with random forests. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, 6–12 September 2014, Proceedings, Part VI 13, pp. 446–461. Springer (2014)
3. Carvalho, M., et al.: Cross-modal retrieval in the cooking context: learning semantic text-image embeddings. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 35–44 (2018)
4. Fu, H., et al.: Mcen: bridging cross-modal gap between cooking recipes and dish images with latent variable model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14570–14580 (2020)
5. Guerrero, R., et al.: Cross-modal retrieval and synthesis (X-MRS): closing the modality gap in shared subspace learning. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 3192–3201 (2021)
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