Frequent occurrences of marine extreme climate and weather events pose significant threats to human life and property, underscoring the practical significance of meteorological data forecasting methods. Notably, significant advancements in meteorological forecasting fields have been achieved by data-driven deep learning techniques, which leverage observed meteorological datasets and employ deep networks to capture complex patterns. However, challenges remain in accurately extracting local details and capturing spatial-temporal correlations when dealing with multiple meteorological forecasting tasks that exhibits diverse temporal and spatial scales. Hence, in this paper, we propose a Multi-Scale Spatial Temporal Transformer (MS-STT) framework to achieve efficient and accurate meteorological data forecasting. Specifically, to achieve more detailed and multi-scale representation of meteorological data, we design the regionally coherent encoding strategy and multi-scale feature aggregation for visual representation. To enhance the multi-scale ability in terms of learning spatial-temporal correlations, we propose a multi-scale spatial-temporal transformer network, which integrates a multi-scale spatial transformer to learn the spatial association between local patches and multi-scale regions and a temporal transformer to learn the temporal dynamic evolution properties. Extensive quantitative and qualitative experiments on three popular spatial temporal forecasting tasks validate the effectiveness of the proposed method. In particular, compared to the representative data-driven deep learning ENSO forecasting method Earthformer, our approach achieves a 3.7% performance improvement with only one-third of the parameters.

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.

Most existing 3D object classification and retrieval algorithms rely on one-off supervised learning on closed 3D object sets and tend to provide rigid convolutional neural networks with little scalability. Such limitations substantially restrict their potential to learn newly emerged 3D object classes continually in the real world. Aiming to go beyond these limitations, we innovatively propose two new and challenging tasks: class-incremental 3D object classification (CI-3DOC) and class-incremental 3D object retrieval (CI-3DOR), the key to which is class-incremental 3D representation learning. It expects the network to update continually to learn new 3D class representations without forgetting the previously learned ones. To this end, we design a novel balanced distillation network(BDNet)that uses a dual supervision mechanism to balance between consolidating old knowledge (stability) and adapting to new 3D object classes (plasticity) carefully. On the one hand, we employ stability-based supervision to retain the stable and discriminative information of old classes that greatly benefit both classification and retrieval tasks. On the other hand, we use plasticity-based supervision to improve the network's generalization for learning new class 3D representations by transferring knowledge from a temporary teacher network to the current model. By properly handling the relationship between the two modules, we achieve a surprising performance improvement. Furthermore, considering there is no available dataset for evaluation, we build two 3D datasets, INOR-1 and INOR-2, to evaluate these two new tasks. Extensive experimental results demonstrate that our method can significantly outperform other state-of-the-art class-incremental learning methods. Even if we store 500-1000 fewer 3D objects than SOTA methods,BDNetstill achieves comparable performance.

Domain adaptation has been extensively explored as a means of transferring knowledge from the labeled source domain to the unlabeled target domain with disparate data distributions. However, the absence of target annotations and significant domain discrepancies pose a great challenge to transfer knowledge directly from source domain to target domain. To address this challenge, we propose a Progressive Fourier Adversarial Domain Adaptation (PFADA) framework, an effective and versatile framework which can generalize across multiple domain adaptation tasks. Firstly, we propose a Fourier-based style transfer strategy to generate a Fourier intermediate domain that incorporates source images with target domain-specific styles, while preserving the domain-invariant representations of the source data. Secondly, we introduce a progressive adversarial domain adaptation approach that utilizes the Fourier intermediate domain to facilitate the learning of domain-invariant representations. Finally, we present cross-domain semantic alignment and discriminative enhancement approach, which effectively guides the learning of discriminative cross-domain representations utilizing labeled source and intermediate domain data. Extensive experimental evaluations consistently validate the superior performance of the proposed method across diverse visual tasks, encompassing multiple domain adaptive image classification and retrieval scenarios.

With the development of deep learning and the widespread application of 3D modeling technology, image-based cross-domain 3D model retrieval has attracted more and more researchers’ attention. Existing methods have achieved success by aligning the feature distributions from different domains. However, previous methods just statistically align the domain-level or class-level feature distributions, leaving sample discriminability a margin to be improved for retrieval. To address this issue, this paper proposes a Hierarchical Deep Semantic Alignment Network (HDSAN) for cross-domain 3D model retrieval, which combines the proposed sample-level semantic enhancement with global domain alignment and class semantic alignment. Concretely, we adopt adversarial domain adaptation at the domain level and dynamically align the class centers of two domains at the class level. To further improve sample discriminability, we design intra-domain and cross-domain triplet center alignment to enhance the semantic representation ability at the sample level. Experiments on two commonly-used cross-domain 3D model retrieval datasets MI3DOR-1 and MI3DOR-2 demonstrate the effectiveness of the proposed method.

Cross-domain 3D model retrieval facilitates the management of explosively emerging unlabeled 3D models with conveniently available 2D images or RGB-D objects, which has attracted more and more attention. The modality gap between query samples (2D images or RGB-D objects) and 3D models makes the task challenging, and adversarial domain adaptation techniques have achieved success in narrowing such gaps. However, existing methods always pay excessive attention to the samples with high discriminability and transferability, whereas the hard samples with rich information are neglected. Accordingly, we propose hierarchical unbiased constraints to make full use of data at semantic level, sample level and feature level to improve the retrieval performance. At semantic level, we utilize maximum F-norm loss to constrain the semantic prediction results of target domain, which takes advantage of more hard samples to reduce ambiguous predictions and enhance discriminability. At sample level, we propose an adaptive triplet center loss to assign less confident samples with a farther negative class, which reliably compacts samples within the same class and expands the distance across different classes. At feature level, we perform SVD (singular value decomposition) for both source features and target features and suppress the relative value of the largest singular value, so that the information of other eigenvectors can be fully utilized to improve transferability. Experiments on two public datasets validate the superiority of the proposed method, and the ablation study analyzes different roles played by these hierarchical unbiased constraints.

Recent advances in 3D modeling technologies such as 3D scanning, reconstruction and printing produce an explosive increasing of 3D models, consequently 3D model management becomes urgent to facilitate related applications such as CAD, VR/AR and autonomous driving. However, we usually lack the labels of the recently emerging 3D models and even have no prior knowledge toward the label set relationship between new datasets and existing labeled datasets, which makes the management challenging. In this paper, a universal cross-domain 3D model retrieval framework is proposed for utilizing the labeled 2D images or 3D models to manage unlabeled 3D models with no prior knowledge about label sets. Specifically, a sample-level weighting mechanism is adopted to automatically detect the samples from the common label set for both domains. Then, both the domain-level and class-level alignments are performed for domain adaptation. Finally, the adapted features are used for 3D model retrieval. We conduct experiments on the cross-domain 3D model retrieval dataset NTU-PSB (PSB-NTU) and image-based 3D model retrieval dataset MI3DOR, and the results validate the superiority and effectiveness of the proposed method.