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.

Under the heavy management on the increasing 3D models, the topic of image-based 3D model retrieval which organizes unlabeled 3D models based on abundant knowledge learned from labeled 2D images has drawn attention. However, prior methods are limited in aligning semantically at corresponding categories of two domains due to the lack of label information in the 3D domain. To this end, this paper proposes an improved semantic representation learning by multiple clustering approach, which improves the reliability of pseudo labels for 3D models, so as to achieve class-level semantic alignment. Specifically, this paper first extracts features for 2D images and 3D models. Then it clusters combining the 3D features with the semantic information from multiple clustering on 3D model features to obtain more reliable target pseudo labels. Extensive experiments have shown that the proposed method has achieved the gain of 3.0%-205.0% averagely for popular retrieval metrics on the benchmark of monocular image-based 3D object retrieval (MI3DOR), and 1.3%-69.7% on another advanced benchmark, MI3DOR-2.