Deep Robust Multilevel Semantic Cross-Modal Hashing
- URL: http://arxiv.org/abs/2002.02698v2
- Date: Tue, 6 Oct 2020 09:32:31 GMT
- Title: Deep Robust Multilevel Semantic Cross-Modal Hashing
- Authors: Ge Song, Jun Zhao, Xiaoyang Tan
- Abstract summary: Hashing based cross-modal retrieval has recently made significant progress.
But straightforward embedding data from different modalities into a joint Hamming space will inevitably produce false codes.
We present a novel Robust Multilevel Semantic Hashing (RMSH) for more accurate cross-modal retrieval.
- Score: 25.895586911858857
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hashing based cross-modal retrieval has recently made significant progress.
But straightforward embedding data from different modalities into a joint
Hamming space will inevitably produce false codes due to the intrinsic modality
discrepancy and noises. We present a novel Robust Multilevel Semantic Hashing
(RMSH) for more accurate cross-modal retrieval. It seeks to preserve
fine-grained similarity among data with rich semantics, while explicitly
require distances between dissimilar points to be larger than a specific value
for strong robustness. For this, we give an effective bound of this value based
on the information coding-theoretic analysis, and the above goals are embodied
into a margin-adaptive triplet loss. Furthermore, we introduce pseudo-codes via
fusing multiple hash codes to explore seldom-seen semantics, alleviating the
sparsity problem of similarity information. Experiments on three benchmarks
show the validity of the derived bounds, and our method achieves
state-of-the-art performance.
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