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- W4207037315 abstract "Deep metric learning has been widely adopted to construct good representations for images and sentences with pair-based loss functions such as contrastive loss and triplet loss. However, these loss functions restrict the effectiveness of learned embedding and face two critical challenges: 1) high bias on account of a large set of uninformative and redundant pairs; 2) low information exploration due to the lack of processing the relation information among pairs. In this paper, we propose the jointly multi-similarity (JMS) loss to address the above challenges with a novel pair weighting strategy that assigns higher weights to the more informative pairs and discards the less informative ones. Specifically, we integrate the relationship information among pairs into a single framework and optimize the JMS loss by considering various information jointly. Furthermore, we extensively compare the JMS loss with other start-of-the-art approaches by conducting multiple experiments on image retrieval and semantic text similarity tasks. The experimental results show that the JMS loss consistently outperforms competitors." @default.
- W4207037315 created "2022-01-26" @default.
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- W4207037315 date "2021-12-01" @default.
- W4207037315 modified "2023-09-24" @default.
- W4207037315 title "Jointly Multi-Similarity Loss for Deep Metric Learning" @default.
- W4207037315 doi "https://doi.org/10.1109/icdm51629.2021.00190" @default.
- W4207037315 hasPublicationYear "2021" @default.
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