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With the vigorous development of multimedia equipment and applications,efficient retrieval of large-scale multi-modal data has become a trendyresearch topic. Thereinto, hashing has become a prevalent choice due to itsretrieval efficiency and low storage cost. Although multi-modal hashing hasdrawn lots of attention in recent years, there still remain some problems. Thefirst point is that existing methods are mainly designed in batch mode and notable to efficiently handle streaming multi-modal data. The second point is thatall existing online multi-modal hashing methods fail to effectively handleunseen new classes which come continuously with streaming data chunks. In thispaper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS).We design novel semantic-enhanced representation for data, which could helphandle the new coming classes, and thereby construct the enhanced semanticobjective function. An efficient and effective discrete online optimizationalgorithm is further proposed for OASIS. Extensive experiments show that ourmethod can exceed the state-of-the-art models. For good reproducibility andbenefiting the community, our code and data are already available insupplementary material and will be made publicly available.

Online Enhanced Semantic Hashing: Towards Effective and Efficient Retrieval for Streaming Multi-Modal Data