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Hashing is an important method for performing efficient similarity search. With the explosive growth of multimodal data, how to learn hashing-based compact representations for multimodal data becomes highly non-trivial. Compared with shallow-structured models, deep models present superiority in capturing multimodal correlations due to their high(More)
—As large-scale multimodal data are ubiquitous in many real-world applications, learning multimodal representations for efficient retrieval is a fundamental problem. Most existing methods adopt shallow structures to perform multimodal representation learning. Due to a limitation of learning ability of shallow structures, they fail to capture the correlation(More)
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