Federated learning aims to collaboratively train a shared global model from multiple clients while preserving data privacy. However, real-world applications often involve clients learning from limited and dynamically arriving data, requiring the global model to classify all encountered classes. This paper introduces federated few-shot class-incremental learning, enabling effective learning of new classes from scarce samples within a decentralized framework. Existing methods suffer from new class bias, where new classes are often misclassified as previously learned ones. Additionally, they face local bias due to non-IID data distribution, which leads client models to focus excessively on their specific local data characteristics. We propose a Decoupled Multi-Prototype Collaboration (DMPC) method to mitigate both biases. First, we introduce a Global Consistency Aggregation mechanism (GCA) that re-weights local prototypes based on their consistency, resulting in more representative global prototypes and effectively eliminating local bias. Second, we design a Multi-Prototype Testing strategy (MPT) that enhances classification accuracy by leveraging both local and global prototypes, thereby mitigating new class bias. More importantly, GCA and MPT exhibit significant synergistic effects. Extensive experiments on three widely used datasets demonstrate the robustness and superiority of our method in bias reduction.

Skeleton-based action recognition technology has gained significant attention and made great progress in recent years. However, the performance of existing methods declines significantly when the quality of skeleton data extracted by pose estimation algorithms varies. To address this issue, this study proposes an instance-specific learning method aimed at enhancing the model’s ability to learn discriminative features when handling skeleton data of varying quality. We introduce a Dynamic Instance Discriminability Assessment (DIDA) mechanism and a Staged Instance Weighting (SIW) strategy. The DIDA mechanism dynamically evaluates the discriminability of instances by combining prior knowledge with feedback from the model during the training process. The SIW strategy adjusts the weights of instances at different training stages based on their discriminability. Notably, our method requires only a minimal increase in computational cost during training and incurs no additional computational overhead during testing compared to baseline models. We utilized Pifpaf and HR-Net pose estimation methods to extract skeleton data of varying quality from the NTU60, NTU120, and HMDB51 video datasets and conducted extensive experimental validation. The results indicate that the proposed method significantly enhances the action recognition performance while maintaining computational efficiency.

Few-shot class-incremental learning is crucial for developing scalable and adaptive intelligent systems, as it enables models to acquire new classes with minimal annotated data while safeguarding the previously accumulated knowledge. Nonetheless, existing methods deal with continuous data streams in a centralized manner, limiting their applicability in scenarios that prioritize data privacy and security. To this end, this paper introduces federated few-shot class-incremental learning, a decentralized machine learning paradigm tailored to progressively learn new classes from scarce data distributed across multiple clients. In this learning paradigm, clients locally update their models with new classes while preserving data privacy, and then transmit the model updates to a central server where they are aggregated globally. However, this paradigm faces several issues, such as difficulties in few-shot learning, catastrophic forgetting, and data heterogeneity. To address these challenges, we present a synthetic data-driven framework that leverages replay buffer data to maintain existing knowledge and facilitate the acquisition of new knowledge. Within this framework, a noise-aware generative replay module is developed to fine-tune local models with a balance of new and replay data, while generating synthetic data of new classes to further expand the replay buffer for future tasks. Furthermore, a class-specific weighted aggregation strategy is designed to tackle data heterogeneity by adaptively aggregating class-specific parameters based on local models performance on synthetic data. This enables effective global model optimization without direct access to client data. Comprehensive experiments across three widely-used datasets underscore the effectiveness and preeminence of the introduced framework. We will release our code at: https://github.com/XuSiang1/F2SCIL-SDD.

This paper addresses the UAV-view geo-localization task, which focuses on bi-directional retrieval between UAV-view and satellite-view images. Generally, existing methods aim to learn image representations that can distinguish between different locations while effectively mitigating the cross-view domain gap. However, these methods often struggle in noisy UAV flight environments, as they fail to account for environmental domain shifts caused by varying weather and lighting conditions. To this end, we propose a novel Environment-Agnostic Geo-Localization (EAGLe) framework, which integrates a dual-objective discriminator and a style mixture module into diverse UAV-view geo-localization networks to enhance their robustness in dynamic environments. Specifically, the dual-objective discriminator not only distinguishes between UAV and satellite views but also identifies various environmental styles in UAV-view images. Through adversarial learning, the dual-objective discriminator encourages the feature encoder to produce features that remain invariant to both viewpoint and environmental variations. Furthermore, the style mixture module is integrated into the feature encoder to extend diversity at the feature level, allowing EAGLe to learn a broader range of environmental styles beyond the training data. Extensive experiments on the University-1652 and SUES-200 datasets demonstrate that the proposed EAGLe significantly improves the reliability of UAV-view geo-localization networks under dynamic and unpredictable environmental conditions, while maintaining inference efficiency.

In recent years, significant progress has been made in Multi-Agent Deep Reinforcement Learning (MADRL) for addressing cooperative decision-making challenges in multi-aircraft air combat tasks. This paper introduces a novel Spatio-Temporal Relationship Graph Structure Learning method (STRGSL), aimed at overcoming the challenges in capturing the complex and dynamic interactions between agents. The proposed STRGSL constructs a historical behavior graph based on past observations as well as a real-time interaction graph from current observations, providing a comprehensive consideration of both immediate and long-term agent relationships. Leveraging a Graph Neural Network (GNN), STRGSL generates agent representations that fuse current and historical relationships. By integrating STRGSL into a MADRL framework, we jointly optimize both the structure of relationship graphs and the cooperative policies of agents. Experiments carried out in an aircraft combat scenario and two multi-agent cooperative scenarios demonstrate that the proposed STRGSL promotes collaboration among multiple agents, thereby enhancing the overall performance across different scenarios.

This letter aims to deal with the UAV-view geolocalization problem, which is essentially to achieve bi-directional cross-view matching between UAV-view and satellite-view images. The existing studies have confirmed the importance of learning part-wise representations for this task. We go a step further by proposing a two-stage learning framework. The first stage focuses on extracting part-wise representations. In the second stage, a novel Adaptive Embedding Network (AEN) integrates these representations into a global embedding of the entire image to avoid an equal influence of all local parts on image similarity measures. Current mainstream methods typically employ CrossEntropy loss to learn location-dependent representations, aiming to push the distance between different locations in the learned representation space. Some approaches also utilize KL loss or Triplet loss to bring a pair of UAV-satellite images from the same location closer for learning view-invariant representations. However, they overlook a critical concern: a notable representation bias exists among UA-view images captured from the same location but at different viewpoints or heights. To address these issues, we devise a novel cross-view matching loss that narrows the distance between the global embeddings of a satellite-view image and the affinity-aware prototype of multiple true-matched UAV-view images. The experimental results on the University1652 dataset indicate that similarity measures in the learned embedding space exhibit excellent generalization to images from new locations, achieving superior cross-view matching performance compared to previous methods

This paper exploits the properties of the commute time between nodes of a graph for the purposes of clustering and embedding, and explores its applications to image segmentation and multi-body motion tracking. Our starting point is the lazy random walk on the graph, which is determined by the heatkernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterize the random walk using the commute time (i.e. the expected time taken for a random walk to travel between two nodes and return) and show how this quantity may be computed from the Laplacian spectrum using the discrete Green's function. Our motivation is that the commute time can be anticipated to be a more robust measure of the proximity of data than the raw proximity matrix. In this paper, we explore two applications of the commute time. The first is to develop a method for image segmentation using the eigenvector corresponding to the smallest eigenvalue of the commute time matrix. We show that our commute time segmentation method has the property of enhancing the intra-group coherence while weakening inter-group coherence and is superior to the normalized cut. The second application is to develop a robust multi-body motion tracking method using an embedding based on the commute time. Our embedding procedure preserves commute time, and is closely akin to kernel PCA, the Laplacian eigenmap and the diffusion map. We illustrate the results both on synthetic image sequences and real world video sequences, and compare our results with several alternative methods.

This paper exploits the properties of the commute time to develop a graphspectral method for image segmentation. Our starting point is the lazy random walk on the graph, which is determined by the heat-kernel of the graph and can be computed from the spectrum of the graph Laplacian. We characterise the random walk using the commute time between nodes, and show how this quantity may be computed from the Laplacian spectrum using the discrete Green’s function. We explore the application of the commute time for image segmentation using the eigenvector corresponding to the smallest eigenvalue of the commute time matrix. 1