Online Enhanced Semantic Hashing: Towards Effective and Efficient
Retrieval for Streaming Multi-Modal Data
- URL: http://arxiv.org/abs/2109.04260v1
- Date: Thu, 9 Sep 2021 13:30:31 GMT
- Title: Online Enhanced Semantic Hashing: Towards Effective and Efficient
Retrieval for Streaming Multi-Modal Data
- Authors: Xiao-Ming Wu, Xin Luo, Yu-Wei Zhan, Chen-Lu Ding, Zhen-Duo Chen,
Xin-Shun Xu
- Abstract summary: We propose a new model, termed Online enhAnced SemantIc haShing (OASIS)
We design novel semantic-enhanced representation for data, which could help handle the new coming classes.
Our method can exceed the state-of-the-art models.
- Score: 21.157717777481572
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the vigorous development of multimedia equipment and applications,
efficient retrieval of large-scale multi-modal data has become a trendy
research topic. Thereinto, hashing has become a prevalent choice due to its
retrieval efficiency and low storage cost. Although multi-modal hashing has
drawn lots of attention in recent years, there still remain some problems. The
first point is that existing methods are mainly designed in batch mode and not
able to efficiently handle streaming multi-modal data. The second point is that
all existing online multi-modal hashing methods fail to effectively handle
unseen new classes which come continuously with streaming data chunks. In this
paper, we propose a new model, termed Online enhAnced SemantIc haShing (OASIS).
We design novel semantic-enhanced representation for data, which could help
handle the new coming classes, and thereby construct the enhanced semantic
objective function. An efficient and effective discrete online optimization
algorithm is further proposed for OASIS. Extensive experiments show that our
method can exceed the state-of-the-art models. For good reproducibility and
benefiting the community, our code and data are already available in
supplementary material and will be made publicly available.
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