Deep Robust Multilevel Semantic Cross-Modal Hashing
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Cross-Modal RetrievalRetrievalTripletSimilar Papers 제목 키워드 기반
Ranking-based Deep Cross-modal Hashing
Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level feat…
Cross-Modal RetrievalRetrievalDeep Semantic Ranking Based Hashing for Multi-Label Image Retrieval
With the rapid growth of web images, hashing has received increasing interests in large scale image retrieval. Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based o…
Image RetrievalMulti-Label Image RetrievalRetrievalSemantic Similarity+1Deep Cross-modal Hashing via Margin-dynamic-softmax Loss
Due to their high retrieval efficiency and low storage cost for cross-modal search task, cross-modal hashing methods have attracted considerable attention. For the supervised cross-modal hashing methods, how to make the …
Cross-Modal RetrievalRetrievalCascade hash tables: a series of multilevel double hashing schemes with O(1) worst case lookup time
In this paper, the author proposes a series of multilevel double hashing schemes called cascade hash tables. They use several levels of hash tables. In each table, we use the common double hashing scheme. Higher level ha…
Task-adaptive Asymmetric Deep Cross-modal Hashing
Supervised cross-modal hashing aims to embed the semantic correlations of heterogeneous modality data into the binary hash codes with discriminative semantic labels. Because of its advantages on retrieval and storage eff…
Cross-Modal RetrievalRetrieval