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Building extraction from remote sensing images holds significant importance in the fields of land resource management, urban planning, and disaster assessment. Encoder-decoder deep learning models are increasingly favored due to their advanced feature representation capabilities in image analysis. However, because of the diversity of architectural styles and issues such as tree occlusion, traditional methods often result in building omissions and blurred boundaries when extracting building footprints. Given these limitations, this paper proposes a cutting-edge Multi-Scale Frequency-Spatial Domain Attention Fusion Network (MFSANet), which consists of two principal modules, named Frequency-Spatial Domain Attention Fusion Module (FSAFM) and Attention-Guided Multi-scale Fusion Upsampling Module (AGMUM). FSAFM introduces frequency domain attention and spatial attention separately to enhance the feature maps, thereby strengthening the model's boundary-detection capabilities and ultimately improving the accuracy of building extraction. AGMUM first resizes and concatenates attention enhancement maps to enhance contextual understanding and applies attention guidance to further improve prediction accuracy. Our model demonstrates superior performance compared to existing semantic segmentation methods on both the WHU building data set and the Inria aerial image data set. [ABSTRACT FROM AUTHOR]

Existing research on multimodal machine translation (MMT) has typically enhanced bilingual translation by introducing additional alignment visual information. However, picture form requirements in multimodal datasets pose important constraints on the development of MMT because this requires a form of alignment between image, source text, and target text. This limitation is especially compounded by the fact that the inference phase, when aligning images, is not directly available in a conventional neural machine translation (NMT) setup. Therefore, we propose an innovative MMT framework called the DSKP-MMT model, which supports machine translation by enhancing knowledge distillation and feature refinement methods in the absence of images. Our model first generates multimodal features from the source text. Then, the purified features are obtained through the multimodal feature generator and knowledge distillation module. The features generated through image feature enhancement are subsequently further purified. Finally, the image–text fusion features are generated as input in the transformer-based machine translation reasoning task. In the Multi30K dataset test, the DSKP-MMT model has achieved a BLEU of 40.42 and a METEOR of 58.15, showing its ability to improve translation effectiveness and facilitating utterance communication. [ABSTRACT FROM AUTHOR]

Although multimodal face data have obvious advantages in describing live and spoofed features, single‐modality face antispoofing technologies are still widely used when it is difficult to obtain multimodal face images or inconvenient to integrate and deploy multimodal sensors. Since the live/spoofed representations in visible light facial images include considerable face identity information interference, existing deep learning‐based face antispoofing models achieve poor performance when only the visible light modality is used. To address the above problems, the authors design a dual‐channel network structure and a constrained representation learning method for face antispoofing. First, they design a dual‐channel attention mechanism‐based grouped convolutional neural network (CNN) to learn important deceptive cues in live and spoofed faces. Second, they design inner contrastive estimation‐based representation constraints for both live and spoofed samples to minimise the sample similarity loss to prevent the CNN from learning more facial appearance information. This increases the distance between live and spoofed faces and enhances the network's ability to identify deceptive cues. The evaluation results indicate that the framework we designed achieves an average classification error rate (ACER) of 2.37% on the visible light modality subset of the CASIA‐SURF dataset and an ACER of 2.4% on the CASIA‐SURF CeFA dataset, outperforming existing methods. The proposed method achieves low ACER scores in cross‐dataset testing, demonstrating its advantage in domain generalisation. [ABSTRACT FROM AUTHOR]

Traditional human pose estimation methods typically rely on complex models and algorithms. Lite-HRNet can achieve an excellent performance while reducing model complexity. However, its feature extraction scale is relatively single, which can lead to lower keypoints' localization accuracy in crowded and complex scenes. To address this issue, we propose a lightweight human pose estimation model based on a joint channel coordinate attention mechanism. This model provides a powerful information interaction channel, enabling features of different resolutions to interact more effectively. This interaction can solve the problem of human pose estimation in complex scenes and improve the robustness and accuracy of the pose estimation model. The introduction of the joint channel coordinate attention mechanism enables the model to more effectively retain key information, thereby enhancing keypoints' localization accuracy. We also redesign the lightweight basic module using the shuffle module and the joint channel coordinate attention mechanism to replace the spatial weight calculation module in the original Lite-HRNet model. By introducing this new module, we not only improve the network calculation speed and reduce the number of parameters of the entire model, but also ensure the accuracy of the model, thereby achieving a balance between performance and efficiency. We compare this model with current mainstream methods on the COCO and MPII dataset. The experimental results show that this model can effectively reduce the number of parameters and computational complexity while ensuring high model accuracy. [ABSTRACT FROM AUTHOR]

With the rapid popularity and continuous development of social networks, users' communication and interaction through platforms such as microblogs and forums have become more and more frequent. The comment data on these platforms reflect users' opinions and sentiment tendencies, and sentiment analysis of comment data has become one of the hot spots and difficulties in current research. In this paper, we propose a BERT-ETextCNN-ELSTM (Bidirectional Encoder Representations from Transformers–Enhanced Convolution Neural Networks–Enhanced Long Short-Term Memory) model for sentiment analysis. The model takes text after word embedding and BERT encoder processing and feeds it to an optimized CNN layer for convolutional operations in order to extract local features of the text. The features from the CNN layer are then fed into the LSTM layer for time-series modeling to capture long-term dependencies in the text. The experimental results proved that compared with TextCNN (Convolution Neural Networks), LSTM (Long Short-Term Memory), TextCNN-LSTM (Convolution Neural Networks–Long Short-Term Memory), and BiLSTM-ATT (Bidirectional Long Short-Term Memory Network–Attention), the model proposed in this paper was more effective in sentiment analysis. In the experimental data, the model reached a maximum of 0.89, 0.88, and 0.86 in terms of accuracy, F1 value, and macro-average F1 value, respectively, on both datasets, proving that the model proposed in this paper was more effective in sentiment analysis of comment data. The proposed model achieved better performance in the review sentiment analysis task and significantly outperformed the other comparable models. [ABSTRACT FROM AUTHOR]