Due to its computational efficiency and low storage requirement, cross-modal hashing (CMH) gains a lot of attention. However, there are still three issues that affect its performance: 1) most existing methods focus on learning the shared semantics between different modalities with the modality-specific semantics ignored; 2) noisy labels may further exacerbate the semantic differences of different modalities during learning; and 3) most existing methods overlook the complementarity between modality-specific labels and semantic features. To address these issues, this work develops a novel CMH method called Dual-Semantic Enhancement Cross-Modal Hashing with Noisy Labels (DSENL). DSENL consists of three parts: (a) Modality-Specific Label Recovery (MSLR) that obtains modality-specific clean labels by applying matrix decomposition with low-rank and sparse constraints to the observed labels; (b) Semantic Preservation under Label Guidance (SPLG) that enhances the quality of recovered labels by using an$l_{2,1}$norm and maintains semantic consistency across modalities by reducing discrepancies among modality-specific labels; and (c) Dual-Semantic Enhancement Learning (DSEL) that integrates both label and sample semantics from modality-specific to enhance the discriminative capability of hash codes. By DSENL, the discriminability of the learned hash codes is improved. Experimental results on four benchmark datasets demonstrate the effectiveness of DSENL. The source code is available athttps://github.com/niuniubit/DSENL.git.

Domain adaptation (DA) is designed to tackle the problem of label scarcity in the target domain by transferring knowledge to it. However, there are two critical challenges in DA : 1) insufficient discriminative power of learned features, and 2) inadequate exploration of inter-sample relationships. This study proposes a novel framework, Joint Consistency-Driven Dual-Domain Discriminative Learning (JCD3L) to overcome these limitations. This framework encompasses two components: Inter-domain Collaborative Feature Enhancement (ID-CFE) and Joint Semantic-Spatial Consistency Constraint (JSSCC). Firstly, ID-CFE applies angular margin (AM) loss to the source domain while imposing entropy regularization on the target domain, establishing a dual-domain discriminative enhancement mechanism for feature representations. Additionally, a novel consistency regularization, JSSCC, is proposed to thoroughly explore the interrelationships among samples. This regularization leverages the label-semantics and feature-semantics to refine the alignment process. To verify the effectiveness of our work, comprehensive experiments are conducted across three widely used benchmarks and the results demonstrate considerable improvements.

With the rapid growth of multi-modal data, Cross-Modal Hashing (CMH) is widely applied due to its outstanding performance in both search and storage. Nevertheless, there are two issues to be further addressed: (1) most existing methods neglect dynamic learning of the importance of different labels; and (2) many methods fail to purify the consistency of data extracted from different feature spaces. For this purpose, we propose a method called Dynamic Label Correlations and Dual-Semantic Enhancement Learning for Cross-Modal Retrieval (DLCDE) in this study. This method is formed of two parts: Label Semantic Enhancement with Dynamic Label Reconstruction (LSEDLR) and Sample Semantic Enhancement with Consistency Purification and Structure Maintenance (SECPSM). The former first utilizes label-wise self-expression to dynamically explore the latent correlations between different labels and then employs a graph-based manifold regularizer to explore the structural relationships in the transformed label space to enhance label semantics, the latter leverages Hadamard-Product-based Matrix Factorization to enhance the common relationships between samples, thereby enhancing the sample semantics of the latent shared space. Moreover, dual-semantic enhancement learning is achieved by integrating enhanced label semantics and sample semantics in Distance-Distance Difference Minimization (DDDM). Numerous experiments on four benchmark datasets reveal that DLCDE surpasses a number of state-of-the-art CMH methods . The source code for DLCDE is publicly available at https://github.com/Fizzyf/DLCDE .

Domain Adaptation (DA) is used to reduce cross-domain differences between the labeled source and unlabeled target domains. As the existing semantic-based DA approaches mainly focus on extracting consistent knowledge under semantic guidance, they may fail in acquiring (a) personalized knowledge between intra-class samples, and (b) local knowledge of neighbor samples from different categories. Hence, a multi-semantic-granularity and target-sample oriented approach, called Adaptive Graph Learning with Semantic Promotability (AGLSP), is proposed, which consists of three parts: (a) Adaptive Graph Embedding with Semantic Guidance (AGE-SG) that adaptively estimates the promotability of target samples and learns variant semantic and geometrical components from the source and those semantically promotable target samples; (b) Semantically Promotable Sample Enhancement (SPSE) that further increases the discriminability and adaptability of tag granularity by mining the features of intra-class source and semantically promotable target samples with multi-granularities; and (c) Adaptive Graph Learning with Implicit Semantic Preservation (AGL-ISP) that forms the tag granularity by extracting commonalities between the source and those semantically non-promotable target samples. As AGLSP learns more semantics from the two domains, more cross-domain knowledge is transferred. Mathematical proofs and extensive experiments on seven datasets demonstrate the performance of AGLSP.

Due to the large-scale and multiscale natures of social media data, sparse representation (SR) learning methods are widely followed. However, there are three problems associated with the existing SR methods: 1) they neglect the fact that the semantic features of data may change during iterative learning, which leads to weak semantic learning; 2) they often assume that the data are linearly separable, while the data might be nonlinear in many real-world applications; and 3) they cannot ensure the low-rank and discriminative properties of the data at the same time and might neglect the global properties of the data, leading to suboptimal solutions. To solve these problems, we propose a novel method, named kernel-based SR learning with global and local low-rank label (KSR-GL3) constraint, which strengthens the semantic information and ensures the semantic features invariant during learning. First, we map the data into a high-dimensional feature space to learn the linear representation of samples. Second, global and local low-rank label (GL3) constraint is used to ensure the semantic invariance, low-rankness, and discrimination of features during learning. Third, an$\ell _{2,1}$is imposed to explore the sparseness of the subspace. Mathematical analyses show that GL3 can retain the intrinsic properties of data during learning. By combining the above three components, a generalized power iteration (GPI) approach is applied to build the model and deal with the tricky optimization problem. By KSR-GL 3, a sparse, low-rank, and discriminative subspace is produced from the high-dimensional and orthogonal representation of the data under the guidance of semantics, while the intrinsic properties of data are preserved. Extensive experiments on six datasets compared with five advanced algorithms demonstrate its promising prospects.

As social media faces with large amounts of data and multimodal properties, cross-modal hashing (CMH) retrieval gains extensive applications with its high efficiency and low storage consumption. However, there are two issues that hinder the performance of the existing semantics-learning-based CMH methods: 1) there exist some nonlinear relationships, noises, and outliers in the data, which may degrade the learning effectiveness of a model; and 2) the complementary relationships between the label semantics and sample semantics may be inadequately explored. To address the above two problems, a method called robust asymmetric cross-modal hashing retrieval with dual semantic enhancement (RADSE) is proposed. RADSE consists of three parts: 1) cross-modal data alignment (CDA) that applies kernel mapping and establishes a unified linear representation in the neighborhood to capture the nonlinear relationships between cross-modal data; 2) relaxed label semantic learning for robustness (RLSLR) that uses a relaxation strategy to expand label distinctiveness, and leverages$\ell_{2,1}$norm to enhance the robustness of the model against noise and outliers; and 3) dual semantic enhancement learning (DSEL) that learns more interrelationships between samples under the label semantic guidance to ensure the mutual enhancement of semantic information. Extensive experiments and analyses on three popular datasets demonstrate that RADSE outperforms the most existing methods in terms of mean average precision (MAP), precision recall (P–R) curves, and top-N precision curves. In the comparisons of MAP, RADSE improves by an average of 2%–3% in two retrieval tasks.