In the semantic segmentation of remote sensing images, methods based on Convolutional Neural Networks (CNN) and Transformers have been extensively studied. Nevertheless, CNN struggles to capture global context due to its local feature extraction, while Transformer is constrained by the complexity of quadratic calculations. Recently, there has been a great deal of interest in Mamba-based state space models. However, the existing Mamba-based methods do not adequately consider the significance of local information in remote sensing image segmentation tasks. In this paper, a codec style network UMFormer is constructed for the semantic segmentation of remote sensing image. Specifically, UMFormer employs the ResNet18 as the encoder, with the objective of performing a preliminary image feature extraction. Subsequently, a self-attention mechanism is optimized to extract the global information pertaining to the objects of disparate sizes within the context of a multi-scale condition. For fusing the codec feature map information, another attention structure is built to reconstruct the space information and to capture the relative position relationship. Finally, a decoder based on Mamba is designed to effectively model both global and local information. Concurrently, a feature fusion mechanism utilizing feature similarity is devised with the objective of embedding local information into global ones. Numerous experiments on UAVid, Vaihingen and Potsdam datasets have demonstrated that the proposed UMFormer exhibits enhanced accuracy while maintaining an efficient running speed. The code will be freely available at https://github.com/takeyoutime/UMFormer.

Artificial Intelligence (AI) is deemed to be the commanding point of science and technology in the next era. In recent years, with the enhancement of computer computing power, the improvement of the quantity and quality of big data, and the important breakthroughs in many research fields such as machine learning and speech recognition, AI technology has developed rapidly and has been widely used in all walks of life. In the financial industry, the application of AI technology in risk control, marketing, customer service, transaction, operation, and product optimization of financial institutions is becoming increasingly mature, and some new business models have been created. Starting from the application status and significance of AI in the international financial field, this paper expounds on the application, status quo, and development trend of AI in the financial industry. Then, in view of the risks and practical challenges existing in the development process of AI, based on the reality of international financial development, this paper summarizes the measures to promote the in-depth, healthy, and sustainable development of AI in the financial market. This paper aims to let readers understand the development status of AI in the financial field, and also provide theoretical reference for scholars in this field.

Remote sensing images have the essential attribute of large-scale spatial variation and complex scene information, as well as the high similarity between various classes and the significant differences within same class, which are easy to cause misclassification. To solve this problem, an efficient systematic architecture named EFCOMFF-Net (Multi-scale Feature Fusion Network with Enhanced Feature Correlation) is proposed to reduce the gap among multi-scale features and fuse them to improve the representation ability of remote sensing images. Firstly, to strengthen the correlation of multi-scale features, a Feature Correlation Enhancement Module (FCEM) is specifically developed, which takes the features of different stages of the backbone network as input data to obtain multi-scale features with enhanced correlation. Considering the differences between the shallow features and the deep features, the EFCOMFF-Net-v1 related to shallow features and EFCOMFF-Net-v2 related to deep features with different structures are proposed. Secondly, the designed two versions of the deep learning network focus on the global contour information and need to encode more accurate spatial information. A Feature Aggregation Attention Module (FAAM) is designed and embedded into the network to encode the deep features by applying the spatial information aggregation features. Finally, considering that the simple integration strategy cannot reduce the gap between the shallow multi-scale features and the deep features, a Feature Refinement Module (FRM) is presented to optimize the network. ResNet50, DenseNet121, and ResNet152 are selected to conduct a considerable number of experiments on four datasets, which show the superiority of our method compared to recent methods.