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Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model
Science and technology big data contain a lot of cross-media information. There are images and texts in the scientific paper. The s ingle modal search method cannot well meet the needs of scientific researchers. This paper proposes a cross-media scientific research achievements retrieval method based on deep language model (CARDL). It achieves a unified cross-media semantic representation by learning the semantic association between different modal data, and is applied to the generation of text semantic vector of scientific research achievements, and then cross-media retrieval is realized through semantic similarity matching between different modal data. Experimental results show that the proposed CARDL method achieves better cross-modal retrieval performance than existing methods.
Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval
In light of this, we propose a scientific and technological information oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval method (SMCR) to find an effective common subspace.
