Feb 7, 2024 · (2023); Zheng et al. (2023) . Compared with unimodal learning which relies on a single data source in one modality, multimodal learning ...
Oct 25, 2023 · We propose a CL training strategy for learning uni-modal encoders suited to multi-modal similarity search for e- commerce. We study ANNS ...
Jul 4, 2023 · Abstract: Multimodal metric learning aims to transform heterogeneous data into a common subspace where cross-modal similarity computing can ...
For unsupervised transfer, we introduce cross-modal similarity matching (CSM) that enables a student model to learn the representations of a teacher model by ...
Full article: Deep similarity learning for multimodal medical images
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An effective similarity measure for multi-modal images is crucial for medical image fusion in many clinical applications. The underlining correlation across ...
A novel deep similarity learning method that trains a binary classifier to learn the correspondence of two image patches to show the advantage of the ...
May 4, 2023 · Methods: In this study, we explore a new data-driven prior knowledge that captures the subject-level similarity by fusing multi-modal similarity ...
Typical research map different modalities into a common subspace with a one-to-one correspondence or similarity/dissimilarity relationship of inter-modal data, ...
Oct 1, 2011 · The goal of this dissertation is to study how to effectively combine multiple modalities to implement similarity search systems for large datasets.
... learning product similarity for highly challenging real-world data from the Amazon catalog. We define it as a metric learning problem, where similar ...