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- W3163367772 abstract "We propose JECL, a method for clustering image-caption pairs by training parallel encoders with regularized clustering and alignment objectives, simultaneously learning both representations and cluster assignments. These image-caption pairs arise frequently in high-value applications where structured training data is expensive to produce, but free-text descriptions are common. JECL trains by minimizing the Kullback-Leibler divergence between the distribution of the images and text to that of a combined joint target distribution and optimizing the Jensen-Shannon divergence between the soft cluster assignments of the images and text. Regularizers are also applied to JECL to prevent trivial solutions. Experiments show that JECL outperforms both single-view and multi-view methods on large benchmark image-caption datasets, and is remarkably robust to missing captions and varying data sizes." @default.
- W3163367772 created "2021-05-24" @default.
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- W3163367772 date "2021-01-10" @default.
- W3163367772 modified "2023-10-11" @default.
- W3163367772 title "JECL: Joint Embedding and Cluster Learning for Image-Text Pairs" @default.
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- W3163367772 doi "https://doi.org/10.1109/icpr48806.2021.9412667" @default.
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