Unsupervised cross-modal hashing has gained significant attention for efficient retrieval between heterogeneous modalities through encoding data into the unified binary representations, offering low storage cost and fast response. However, the constraints of existing methods persist in bridging the cross-modal semantic gap and capturing fine-grained global semantic structures without explicit labels. In this paper, we propose an innovative unsupervised Stationary distribution and soft Clustering Transformer Hashing approach for cross-modal retrieval, denoted as SCTH. Initially, a Transformer-based modality fusion encoder is employed to extract abundant cross-modal semantic representations, further integrated with contrastive hashing to minimize the semantic gap. To enhance the inter-modal alignment, a pseudo-classifier clustering module with entropy-regularized contrastive loss is presented, ensuring balanced and diverse cluster assignments in unsupervised settings. Additionally, a Markovian stationary distribution strategy stabilizes the feature representations through mitigating the interference of noise and outliers. Comprehensive experiments on MIRFlickr, NUS-WIDE, and IAPR-TC12 datasets validate that SCTH outperforms state-of-the-art hashing methods in cross-modal retrieval tasks, demonstrating superior generalization performance.

Hashing has been widely applied in large-scale multimodal retrieval by mapping heterogeneous modalities data into binary codes. However, most cross-modal hashing methods cannot make the most of semantic information to construct the association relations of sample pairs, resulting in unsatisfactory retrieval accuracy. Concept lattice is a powerful tool for data mining and information retrieval, and for all we know, this is the first time to combine formal concept analysis and hash learning to improve cross-modal hashing retrieval performance. In this paper, we propose a novel framework for Asymmetric Cross-modal Hashing based on Formal Concept Analysis, denoted as ACHFCA. Initially, a flash-projection three-layer semantic enhancement descriptor is designed to extract latent representations from heterogeneous modalities. Subsequently, an asymmetric hash learning framework is established to enhance the semantics of different layers based on the fine-grained similarity values reconstructed from concept lattice to reinforce the discriminative competence of the model. Finally, an effective discrete optimization algorithm is proposed, which can directly learn compact hash codes. Comprehensive experiments on MIRFlickr, NUS-WIDE and IAPR-TC12 datasets demonstrate the superior performance of ACHFCA to state-of-the-art hashing approaches.

Unsupervised hashing is applied in large-scale multimodal retrieval by mapping original data from heterogeneous modalities into compact binary codes. Transformer-based retrieval augmented generation possesses significant advantages in retrieval accuracy and context-awareness, yet faces scalability challenges due to the computational overhead of dense embedding. Thus, the integration of hash learning and Transformer provides a feasible improvement scheme, which can achieve efficient retrieval preserving semantic association. This paper proposes a novel Unsupervised Similarity-Fusion Transformer Hashing for multimodal retrieval, denoted as USFTH. Initially, the modal fusion similarity matrix based on Gaussian kernel, sigmoid function, and Laplacian transformation is introduced to construct a discriminative similarity matrix, ensuring that semantic correlation among samples can be captured precisely. Then, cross-modal multiplex joint construction via Transformer-based attention mechanisms is designed, realizing effective integration of heterogeneous modalities in the similarity matrix through multi-path fusion. Furthermore, the consensus fusion strategy is proposed to ensure that hash codes generated under unsupervised conditions possess a uniform distribution and achieve accurate retrieval. In addition, comprehensive experiments on MIRFlickr, NUS-WIDE, and IAPR-TC12 datasets demonstrate the superior performance of USFTH to state-of-the-art hashing approaches.

Unsupervised hashing has been extensively applied in large-scale multi-modal retrieval by mapping original data from heterogeneous modalities into unified binary codes . However, there still remain challenges especially how to balance the individual modality-specific representations and common representation preserving intrinsic linkages among heterogeneous modalities. In this paper, we propose a novel fast U nsupervised M ulti-modal H ashing based on P iecewise L earning, denoted as UMHPL , to deal with the mentioned issue. Initially, we formulate the problem as matrix factorization to derive the individual modality-specific latent representations and common latent representation with consensus matrices in a brief time. To maintain the integrality of multi-modal data, we integrate them by adaptive weight factors and nuclear norm minimization. Subsequently, we establish a connection between the individual modality-specific latent representations and common latent representation based on the piecewise hash learning framework to reinforce the discriminative competency of model, which leads the hash codes more compact. Finally, an effective discrete optimization algorithm in mathematical logic and functional analysis is proposed. Comprehensive experiments on Wiki, MIRFlirck, NUS-WIDE, and MSCOCO datasets demonstrate the superior performance of UMHPL to state-of-the-art hashing methods.

Uncertainties are widespread in real-world situations. In the existing studies, the uncertainties expressed by the agents engaged in a conflict situation have been extensively investigated. However, to what extent these agents can be trusted is often ignored and seldom taken into account. In this article, to manage these two sides of uncertainties, we propose a mathematical framework for multilevel conflict analysis from an outsider's perspective. Concretely, in order to indicate the analysis levels and manage the conflict situation with an extent-intent view, we resort to fuzzy formal concept analysis. At first, we explain what is multilevel data analysis. After that, based on fuzzy concept lattices, we present a general framework for multilevel conflict analysis and a method for computing the maximal coalitions and minimum conflict sets. And then, we describe how to update conflict analysis results when increasing analysis levels. Experimental analysis demonstrates that managing two sides of uncertainties can bring much more conflict resolution, and increasing analysis levels is helpful for postponing decision making and bringing more economical conflict resolutions. Finally, case studies are made to show the application of multilevel conflict analysis model and some interesting findings are obtained. Specifically, once the cost of increasing analysis levels is taken into consideration, it is interesting to choose the optimal analysis level for making the most economic decision. The main contribution of this study is the model of multilevel conflict analysis based on fuzzy concept lattices, which can incorporate the uncertainties of two sides.