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Cross-domain Food Image-to-Recipe Retrieval by Weighted Adversarial Learning by Zhu, Bin; Ngo, Chong-Wah; Chen, Jingjing; Chan, Wing-Kwong is a scholarly article available to read on EtoBox.

What is Cross-domain Food Image-to-Recipe Retrieval by Weighted Adversarial Learning about?

Food image-to-recipe aims to learn an embedded space linking the rich semantics in recipes with the visual content in food image for cross-modal retrieval. The existing research works carry out the learning of such space by assuming that all the image-recipe training example pairs belong to the same cuisine. As a result, despite the excellent performance reported in the literature, such space is not transferable for retrieving recipes of different cuisine. In this paper, we aim to address this issue by cross-domain food image-to-recipe retrieval, such that by leveraging abundant image-recipe pairs in source domain (one cuisine), the embedding space is generalizable to a target domain (the other cuisine) that does not have images to pair with recipes for training. With the intuition that the importance of different source samples should vary, this paper proposes two novel mechanisms for cross-domain food image-to-recipe retrieval, i.e., source data selector and weighted cross-modal adversarial learning. The former aims to select source samples similar to the target data and filter out distinctive ones for training. The latter is capable to assign higher weights to the source samples

Author

Zhu, Bin; Ngo, Chong-Wah; Chen, Jingjing; Chan, Wing-Kwong

Published

2023

Language

EN