With the advancements in deep learning and Synthetic Aperture Radar (SAR) technology, an increasing number of individuals are utilizing deep learning techniques to detect ships in SAR images. However, the efficiency of SAR ship detection is affected by complex background interference and various ship sizes. Addressing these challenges, this paper proposes a balanced feature-enhanced attention model (BFEA). First, we introduce a novel Attention Feature Fusion Network (WEF-Net) tailored for SAR multi-scale ship detection. WEF-Net effectively balances the information across different backbone layers and harmonizes semantic information from various levels of the feature pyramid through aggregation and averaging. Next, we embed the receiving field extension module (MRFFM) in WEF-Net to learn the context information and generate the global characteristics of the receiving field balance. In addition, it can extract features from multiple scales to enhance the detection capability of the model for ships of different scales. At the same time, acknowledging the impact of surrounding complex background interference on the detector, we redesigned the ELAN module by combining convolution and attention, This enhancement enables the model to better attend to target position information during feature fusion, suppress the surrounding complex background interference, and highlight the ship's feature information. Finally, owing to the prevalence of small targets in SAR images, we employ an optimized loss function to bolster the model's performance in detecting small targets. This approach accelerates training convergence, reduces instances of missed detection on small targets, and enhances overall detection performance across multiple scales. Experimental results demonstrate that our model achieves detection accuracies of 98%, 93.1%, and 76.9% on the SSDD, HRSID, and LS-SSDD-v1.0 datasets, respectively, effectively discerning ship targets amidst complex backgrounds in SAR images.

Aspect-based sentiment analysis (ABSA) through joint task learning aims to simultaneously identify aspect terms and predict their sentiment polarities. However, existing methods face two major challenges: (1) Most existing studies focus on the sentiment polarity classification task, ignoring the critical role of aspect term extraction, leading to insufficient performance in capturing aspect-related information; (2) existing methods typically model the two tasks independently, failing to effectively share underlying features and semantic information, which weakens the synergy between the tasks and limits the overall performance of the model. In order to resolve these issues, this research suggests a unified framework model through joint task learning, named MTL-GCN, to simultaneously perform aspect term extraction and sentiment polarity classification. The proposed model utilizes dependency trees combined with self-attention mechanisms to generate new weight matrices, emphasizing the locational information of aspect terms, and optimizes the graph convolutional network (GCN) to extract aspect terms more efficiently. Furthermore, the model employs the multi-head attention (MHA) mechanism to process input data and uses its output as the input to the GCN. Next, GCN models the graph structure of the input data, capturing the relationships between nodes and global structural information, fully integrating global contextual semantic information, and generating deep-level contextual feature representations. Finally, the extracted aspect-related features are fused with global features and applied to the sentiment classification task. The proposed unified framework achieves state-of-the-art performance, as evidenced by experimental results on four benchmark datasets. MTL-GCN outperforms baseline models in terms of F1_ATE, accuracy, and F1_SC metrics, as demonstrated by experimental results on four benchmark datasets. Additionally, comparative and ablation studies further validate the rationale and effectiveness of the model design.