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• W2990135557 abstract "We propose a novel non-parametric method for cross-modal retrieval which is applied on top of precomputed image and text embeddings. By combining our method with standard approaches for building image and text encoders, trained independently with a self-supervised classification objective, we create a baseline model which outperforms most existing methods on a challenging image-to-recipe task. We also use our method for comparing image and text encoders trained using different modern approaches, thus addressing the issues hindering the developments of novel methods for cross-modal recipe retrieval. We demonstrate how to use the insights from model comparison and extend our baseline model with standard triplet loss that improves SoTA on the Recipe1M dataset by a large margin, while using only precomputed features and with much less complexity than existing methods." @default.
• W2990135557 created "2019-12-05" @default.
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• W2990135557 date "2019-11-28" @default.
• W2990135557 modified "2023-09-27" @default.
• W2990135557 title "Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA" @default.
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• W2990135557 hasPublicationYear "2019" @default.
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