The pressing need for low storage and high efficiency has significantly propelled the advancement of deep hashing techniques in the realm of large-scale search and retrieval tasks. As one of the most prevailing forms of user-generated contents, micro-videos usually represent more complicated multi-modal behaviors that are further challenged in multi-label retrieval. Existing multi-modal hashing methods tend to prioritize the complementarity and consistency in multi-modal fusion, while neglecting the completeness problem. In this paper, we propose a deep multi-modal hashing with semantic enhancement (DMHSE) method that effectively integrates complete multi-modal representation learning with discriminative binary coding by means of collaboration between two distinct encoders, FoldCoder and HashCoder. FoldCoder translates latent multi-modal representation learning to a degradation process through mimicking data transmitting. Further, it incorporates a prompt learning paradigm to maximize the utilization of multi-label semantics for guiding representation learning. HashCoder combines pairwise and central constraints to ensure more discriminative hashing results. Pairwise constraint preserves the original local relevance structure, while central constraint tackles the problem of semantic ambiguity in multi-label data by leveraging the global label distribution. Experimental results demonstrate that DMHSE achieves superior performance in multi-label micro-video retrieval tasks.

This study delves into multi-variable meteorological spatial-temporal prediction, focusing on the simultaneous forecasting of key meteorological parameters such as temperature, wind speed, and atmospheric pressure. The core challenge of this task lies in identifying commonalities across different variables while capturing their unique features and the interactions among them. To address this, we propose a novel multi-task learning framework tailored for multi-variable meteorological forecasting. Our framework integrates a convolutional variable-specific visual representation module and a variable-interactive spatial-temporal inference module. The former extracts distinct variable information independently for each variable, while the latter employs a tri-level attention mechanism across space, time, and variables to uncover both commonalities and interactions among the variables. An adaptive multi-loss optimization strategy and a local information aggregation module are introduced to balance task optimization complexities and enhance representation stability. Comprehensive experiments across various meteorological prediction tasks confirm the effectiveness of our methods, showcasing superior performance over existing approaches.

Recently, a prevailing trend of user generated content (UGC) on social media sites is the emerging micro-videos. Microvideos afford many potential opportunities ranging from network content caching to online advertising, yet there are still little efforts dedicated to research on micro-video understanding. In this paper, we focus on popularity prediction of micro-videos by presenting a novel low-rank multi-view embedding learning framework. We name it as transductive low-rank multi-view regression (TLRMVR), and it is capable of boosting the performance of micro-video popularity prediction by jointly considering the intrinsic representations of the source and target samples. In particular, TLRMVR integrates low-rank multi-view embedding and regression analysis into a unified framework such that the lowest-rank representation shared by all views not only captures the global structure of all views, but also indicates the regression requirements. The framework is formulated as a regression model and it seeks a set of view-specific projection matrices with low-rank constraints to map multi-view features into a common subspace. In addition, a multi-graph regularization term is constructed to improve the generalization capability and further prevents the overfitting problem. Extensive experiments conducted on a publicly available dataset demonstrate that our proposed method achieve promising results as compared with state-of-the-art baselines.