Multimodal clustering (MMC) overcomes the limitations of unimodal methods by integrating information from multiple sources, but the complexity of heterogeneous information coupling hinders effective feature extraction. Critically, existing MMC paradigms primarily focus on capturing consensus through coarse-grained cross-modal alignment. However, such task-agnostic strategies overlook the differences in the utility of feature information across varying task environments. In the absence of task-centric guidance, models often struggle to effectively distinguish task-relevant critical information from task-irrelevant redundant noise during the disentanglement process, leading to information confusion in the representation space. To address this challenge, we propose a deep disentangled multimodal clustering method guided by information theory, named DRLMMC, which employs a tripartite information optimization mechanism to achieve deep disentanglement of cross-modal representations. 1) We design modality-specific encoders to construct nonlinear mapping spaces, transforming the reconstruction mechanism of autoencoders into an information-theoretic mutual information (MI) constraint problem, preserving the unique features of different modalities; 2) To establish cross-modal semantic associations, it constructs a cross-modal shared information extraction module, and, based on an information-theoretic framework, designs an optimization objective function to progressively align multimodal feature subspaces through MI maximization and contrastive learning, capturing task-relevant invariant features across modalities; 3) A unique information dynamic perception module is proposed, which employs a conditional MI projection network combined with learning distribution regularization to adaptively extract and enhance modality-specific task-relevant unique information. Experimental results demonstrate that DRLMMC outperforms existing state-of-the-art methods on multimodal benchmark datasets, exhibiting excellent generalization ability. Notably, it achieves precise disentanglement of cross-omics features in multi-omics analysis, offering a novel methodological approach for handling complex biomedical data.

Multi-View Clustering (MVC) has gained increasing attention due to its ability to effectively leverage the complementary information of multi-view data. Despite the success of existing MVC methods in many real-world applications, they often overlook the discrepancy of view-specific latent distribution and struggle to ensure the completeness of the multi-view data. To address these challenges and harness the powerful feature extraction capability of deep networks, we propose a novel Contrastive and Dual Adversarial Representation Learning method for Multi-view Clustering, termed as CDARL, to solve multi-view clustering problems with both complete and incomplete multi-view data. Specifically, CDARL employs alternating adversarial and contrastive learning to align the view-specific representations, driving them into the same semantic latent space to minimize the discrepancy in view-specific distributions. In addition, a consensus latent representation is learned by an adaptive fusion block that integrates information from multiple views. The consensus representation is further refined through adversarial learning modeling the transformation of the standard Gaussian distribution to the original data distribution. Moreover, the proposed method incorporates an imputation strategy designed to handle the incomplete multi-view data clustering task. This strategy utilizes both reconstructed samples and cross-view neighbors to impute missing views from the latent space and the original space, thereby preserving clustering information, which ensures the quality and feasibility of the imputed samples. Experimental results on six widely used datasets have verified the competitiveness of the proposed CDARL method against state-of-the-art methods in MVC problems with complete and incomplete multi-view data. Code is available athttps://github.com/xywy220/CDARL-MVC.

With 3D data rapidly emerging as an important form of multimedia information, 3D human mesh recovery technology has also advanced accordingly. However, current methods mainly focus on handling humans wearing tight clothing and perform poorly when estimating body shapes and poses under diverse clothing, especially loose garments. To this end, we make two key insights: (1) tailoring clothing to fit the human body can mitigate the adverse impact of clothing on 3D human mesh recovery, and (2) utilizing human visual information from large foundational models can enhance the generalization ability of the estimation. Based on these insights, we propose ClothHMR, to accurately recover 3D meshes of humans in diverse clothing. ClothHMR primarily consists of two modules: clothing tailoring (CT) and FHVM-based mesh recovering (MR). The CT module employs body semantic estimation and body edge prediction to tailor the clothing, ensuring it fits the body silhouette. The MR module optimizes the initial parameters of the 3D human mesh by continuously aligning the intermediate representations of the 3D mesh with those inferred from the foundational human visual model (FHVM). ClothHMR can accurately recover 3D meshes of humans wearing diverse clothing, precisely estimating their body shapes and poses. Experimental results demonstrate that ClothHMR significantly outperforms existing state-of-the-art methods across benchmark datasets and in-the-wild images. Additionally, a web application for online fashion and shopping powered by ClothHMR is developed, illustrating that ClothHMR can effectively serve real-world usage scenarios. The code and model for ClothHMR are available at: https://github.com/starVisionTeam/ClothHMR.

Spatial transcriptomics technology fully leverages spatial location and gene expression information for spatial clustering tasks. However, existing spatial clustering methods primarily concentrate on utilizing the complementary features between spatial and gene expression information, while overlooking the discriminative features during the integration process. Consequently, the discriminative capability of node representation in the gene expression features is limited. Besides, most existing methods lack a flexible combination mechanism to adaptively integrate spatial and gene expression information. To this end, we propose an end-to-end deep learning method named MAFN for spatially resolved transcriptomics data clustering via a multi-view adaptive fusion network. Specifically, we first adaptively learn inter-view complementary features from spatial and gene expression information. To improve the discriminative capability of gene expression nodes by utilizing spatial information, we employ two GCN encoders to learn intra-view specific features and design a Cross-view Correlation Reduction (CCR) strategy to filter the irrelevant information. Moreover, considering the distinct characteristics of each view, a Cross-view Attention Module (CAM) is utilized to adaptively fuse the multi-view features. Extensive experimental results demonstrate that the proposed MAFN achieves competitive performance in spatial domain identification compared to other state-of-the-art ones.

Fliggy of Alibaba group is one of the largest online travel platform (OTPs) in China, which provides travel products and travel experiences for tens of millions of online users by the personalized recommendation system (RS). User's future travel intent prediction is one key problem in travel scenario, which decides where and what to recommend, e.g., traveling to a surrounding city or a distant city. Such travel intent prediction problem has a lot of important applications, e.g., to push a notification with surrounding scenic spots recommendation to a user with intent to travel around, or to enable personalized promotion strategies to users with different intents. Existing studies on user's intent are largely sub-optimal for users' travel intent prediction at OTPs, since they rarely pay attentions to the characteristics of the travel industry, namely, user behavior sparsity due to low frequency of travel, spatial-temporal periodicity patterns, and the correlations between user's online and offline behaviors. In this paper, to address these challenges, we propose a dual channel intent evolution network based online-offline periodicity-aware network, DCIEN, for user's future travel intent prediction. In particular, it consists of two basic components including 1) Spatial-temporal Intent Patterns Network(ST-IPN), which exploits users' periodic intent patterns from offline data based on convolutional neural networks; 2) Periodicity-aware Intent Evolution Network(PA-IEN), which captures user's instant intent from online behaviors data and the interactions between online and offline intents. Extensive offline and online experiments on a real-world OTP demonstrate the superior performance of DCIEN over state-of-the-art methods.

Land-use maps are instrumental to inform urban planning and environmental research. Street view images (SVIs) have shown great potential for automated land-use classification for land-use mapping. However, previous studies overlooked SVI-derived location contextual information that may help improve land-use classification. This study proposes a novel land-use classification method that synthesizes location semantics from SVIs to account for contextual information from SVIs, land parcels and roads around the SVIs. The proposed method first generates land-use scene images (LUSIs) by using an SVI-derived straightforward algorithm. The LUSIs are then relocated to land parcels by using a displacement strategy and classified into land-use types by using a deep learning network. This study determines the land-use types of land parcels with classified LUSIs. Two case studies, consisting of LUSIs for five land-use types, show that introducing location semantics of SVIs can remarkably improve the classification accuracy of land-use types.