Weakly supervised point cloud semantic segmentation (WSPCSS) has gained attention for reducing reliance on densely annotated data. However, inefficiencies in utilizing sparse annotations hinder comprehensive understanding of complex scenes. Inspired by masked autoencoder (MAE) techniques in image processing, researchers have adapted these methods to WSPCSS. Yet, current 3D masking strategies often fail to capture intricate geometric properties, resulting in generated to-be-filled content that inaccurately represents the underlying 3D scene structure. To address these limitations, this study proposes a novel stereo-wise masking strategy, which extends 2D plane masking into 3D space to generate coherent and semantically rich masked regions with contextual relevance. These regions serve as high-quality learning targets, enabling the model to better comprehend complex point cloud structures. Experimental results demonstrate that, at a 0.01 % annotation density, the proposed method achieves improvements in mIoU by 1.9 % and 4.72 % on the indoor datasets S3DIS and ScanNet V2, respectively, compared to previous methods. Furthermore, at a 0.1% annotation density on the forest dataset For-Instance, the method exhibits a 0.29 % improvement. These results substantiate the effectiveness and stability of the stereo-wise masking strategy.
With the rapid deployment of edge devices, there has been growing concern over the privacy preservation of clients’ private data. Federated learning (FL) has emerged as a promising solution that enables edge devices for collaborative model training without compromising data privacy, as the local datasets of clients are not shared directly. However, previous works have shown that the performance of conventional FL approaches significantly deteriorates when the data distributions across clients are heterogeneous or non-IID. Integrating personalization into FL addresses these challenges by adapting the global model to local data distributions, but existing personalized FL (PFL) frameworks often fail to generalize when clients’ datasets have cross-domain features. One approach to mitigate this challenge is partial model initialization, where to update clients’ local model only a subset of model parameters are initialized from the global model while retaining local parameters. However, obtaining effective generalization across distinct data distributions remains a challenge. In such instances, meta-learning is preferred as it provides the adaptation of a global model across distinct data distributions and tasks by employing only a small number of data samples. In this paper, we propose MetaPartialFL, a novel framework that integrates meta-learning with partial model initialization to address cross-domain personalization and heterogeneity in FL. The approach partitions model parameters into shared global and client-specific local components, enabling effective generalization across clients while maintaining adaptability to each client’s datasets. Extensive evaluations on cross-domain benchmark datasets, such as Office-Home, Office-31, PACS, and DomainNet, demonstrate that MetaPartialFL outperforms state-of-the-art methods, making it a robust and efficient solution for cross-domain FL applications.
This research investigates the application of Large Language Models (LLMs) for automating the assessment of bicycle insurance claims. The study aims to determine the effectiveness of LLMs in interpreting insurance policy documents and evaluating the validity of claims. The ultimate objective being to enhance consistency and efficiency in claims processing by leveraging LLM capabilities to understand complex legal language. The methodology involves training and testing various models on a bicycle insurance policy and hypothetical claims against the policy. Key achievements include the introduction of an agent-based approach that significantly improves the acceptability of written claims handler responses. The findings indicate that, by using a combination of agents with crafted prompts, LLMs can produce acceptable claims handler responses to claim descriptions.
Short-term traffic speed prediction under limited historical data is crucial for intelligent transportation systems, enabling real-time traffic management and congestion mitigation. However, existing methods relying on long lookback window to forecast struggle to capture scenarios where the current traffic state depends more on recent patterns than on distant historical data. To address this challenge, we propose the Spatiotemporal Visibility Graph Network (STVGN), a novel framework that combines visibility graph theory with Graph Neural Networks (GNN). The core of STVGN is the STVGN-Embedding layer, which is designed to enhance short-term spatiotemporal feature representation. This layer leverages the visibility graph's ability to capture transient patterns and integrates GNNs to model intrinsic relationships within the visibility graph derived from time series data. In Combination with a transformer architecture, STVGN-Embedding extracts complex spatiotemporal features, while the transformer uncovers inherent relationships between temporal and spatial dimensions. To the best of our knowledge, this is the first study to introduce visibility graph theory as an embedding layer within a transformer framework. Experiments on three benchmark datasets, covering urban and freeway traffic scenarios, demonstrate STVGN's effectiveness, achieving state-of-the-art performance with improvements of 4.8%-16.8% in RMSE and 8.4%-13.3% in MAE over existing SOTA methods. These results highlight the potential of visibility graph-based embeddings to address challenges posed by limited historical data and to capture intricate traffic patterns.
