Global Context Features Research Articles

Multi-modal Sarcasm Detection on Social Media via Multi-Granularity Information Fusion

The rising popularity of diverse social media platforms, commonly utilized by individuals to articulate their emotions in everyday interactions, has spurred a growing interest in the task of multi-modal sarcasm detection (MSD). Nonetheless, due to the unique nature of sarcasm, there remain two main challenges to achieving robust MSD. Firstly, prevailing methodologies often overlook the issue of weak multi-modal correlation, thereby neglecting the crucial sarcasm cues that are inherent in each uni-modal source. Secondly, there are inefficiencies in modeling cross-modal interactions in unaligned multi-modal data. To tackle these challenges, we introduce a multi-granularity information fusion network for multi-modal sarcasm detection. Specifically, we design a multi-task CLIP framework that can utilize multi-granularity cues from multiple tasks (i.e., text, image, and text-image interaction tasks) for multi-modal sarcasm detection. Furthermore, we devise a global-local cross-modal interaction learning method that uses discourse-level representations from each modality as the global multi-modal context to engage with local uni-modal features. This enables the global multi-modal context and local uni-modal features to mutually enhance and progressively improve through a multi-layer superposition. Following extensive experimental results and thorough analysis, our model achieves state-of-the-art performance in multi-modal sarcasm detection.

Deep-AMPpred: A Deep Learning Method for Identifying Antimicrobial Peptides and Their Functional Activities.

Antimicrobial peptides (AMPs) are small peptides that play an important role in disease defense. As the problem of pathogen resistance caused by the misuse of antibiotics intensifies, the identification of AMPs as alternatives to antibiotics has become a hot topic. Accurately identifying AMPs using computational methods has been a key issue in the field of bioinformatics in recent years. Although there are many machine learning-based AMP identification tools, most of them do not focus on or only focus on a few functional activities. Predicting the multiple activities of antimicrobial peptides can help discover candidate peptides with broad-spectrum antimicrobial ability. We propose a two-stage AMP predictor deep-AMPpred, in which the first stage distinguishes AMP from other peptides, and the second stage solves the multilabel problem of 13 common functional activities of AMP. deep-AMPpred combines the ESM-2 model to encode the features of AMP and integrates CNN, BiLSTM, and CBAM models to discover AMP and its functional activities. The ESM-2 model captures the global contextual features of the peptide sequence, while CNN, BiLSTM, and CBAM combine local feature extraction, long-term and short-term dependency modeling, and attention mechanisms to improve the performance of deep-AMPpred in AMP and its function prediction. Experimental results demonstrate that deep-AMPpred performs well in accurately identifying AMPs and predicting their functional activities. This confirms the effectiveness of using the ESM-2 model to capture meaningful peptide sequence features and integrating multiple deep learning models for AMP identification and activity prediction.

Spatial Attention-Based Kernel Point Convolution Network for Semantic Segmentation of Transmission Corridor Scenarios in Airborne Laser Scanning Point Clouds

Accurate semantic segmentation in transmission corridor scenes is crucial for the maintenance and inspection of power infrastructure, facilitating the timely detection of potential hazards. In this study, we propose SA-KPConv, an advanced segmentation model specifically designed for transmission corridor scenarios. Traditional approaches, including Random Forest and point-based deep learning models such as PointNet++, demonstrate limitations in segmenting critical infrastructure components, particularly power lines and towers, primarily due to their inadequate capacity to capture complex spatial relationships and local geometric details. Our model effectively addresses these challenges by integrating a spatial attention module with kernel point convolution, enhancing both global context and local feature extraction. Experiments demonstrate that SA-KPConv outperforms state-of-the-art methods, achieving a mean Intersection over Union (mIoU) of 89.62%, particularly excelling in challenging terrains such as mountainous areas. Ablation studies further validate the significance of our model’s components in enhancing overall performance and effectively addressing class imbalance. This study presents a robust solution for semantic segmentation, with considerable potential for monitoring and maintaining power infrastructure.

MFHARFNet: multi-branch feature hybrid and adaptive receptive field network for image segmentation

Abstract Accurate segmentation of medical images is crucial for disease diagnosis and understanding disease changes. Deep learning methods, utilizing encoder-decoder structures, have demonstrated cutting-edge performance in various medical image segmentation tasks. However, the pooling operation in the encoding stage results in feature loss, which makes the network lack the ability to fuse multi-scale information at different levels, hinders its effective perception of multi-scale information, and leads to poor segmentation performance. Drawing inspiration from the U-shaped network, this study introduces a multi-branch feature hybrid attention and adaptive receptive field network (MFHARFNet) for medical image segmentation. Building upon the encoder-decoder framework, we initially devise a multi-branch feature hybrid attention module (MFHAM) to seamlessly integrate feature maps of varying scales, capturing both fine-grained features and coarse-grained semantics across the entire scale. Furthermore, we redesign the skip connection to amalgamate feature information from different branches in the encoder stage and efficiently transmit it to the decoder, providing the decoder with global context feature maps at different levels. Finally, the adaptive receptive field (ARF) module is introduced in the decoder feature reconstruction stage to adapt and focus on related fields, ensuring the model’s adaptation to different segmentation target features, and achieving different weights for the output of different convolution kernels to improve segmentation performance. We comprehensively evaluate our method on medical image segmentation tasks, by using four public datasets across CT and MRI. Remarkably, MFHARFNet method consistently outperforms other state-of-the-art methods, exceeding UNet by 2.1%, 0.9%, 6.6% and 1.0% on Dice on ATLAS, LiTs, BraTs2019 and Spine and intervertebral disc datasets, respectively. In addition, MFHARFNet minimizes network parameters and computational complexity as much as possible. The source codes are in https://github.com/OneHundred99/MFHARFNet.

CTFNet: Long-Sequence Time-Series Forecasting Based on Convolution and Time-Frequency Analysis.

Although current time-series forecasting methods have significantly improved the state-of-the-art (SOTA) results for long-sequence time-series forecasting (LSTF), they still have difficulty in capturing and extracting the features and dependencies of long-term sequences and suffer from information utilization bottlenecks and high-computational complexity. To address these issues, a lightweight single-hidden layer feedforward neural network (SLFN) combining convolution mapping and time-frequency decomposition called CTFNet is proposed with three distinctive characteristics. First, time-domain (TD) feature mining-in this article, a method for extracting the long-term correlation of horizontal TD features based on matrix factorization is proposed, which can effectively capture the interdependence among different sample points of a long time series. Second, multitask frequency-domain (FD) feature mining-this can effectively extract different frequency feature information of time-series data from the FD and minimize the loss of data features. Integrating multiscale dilated convolutions, simultaneously focusing on both global and local context feature dependencies at the sequence level, and mining the long-term dependencies of the multiscale frequency information and the spatial dependencies among the different scale frequency information, break the bottleneck of data utilization, and ensure the integrity of feature extraction. Third, highly efficient-the CTFNet model has a short training time and fast inference speed. Our empirical studies with nine benchmark datasets show that compared with state-of-the-art methods, CTFNet can reduce prediction error by 64.7% and 53.7% for multivariate and univariate time series, respectively.

Improving Antifreeze Proteins Prediction with Protein Language Models and Hybrid Feature Extraction Networks.

Accurate identification of antifreeze proteins (AFPs) is crucial in developing biomimetic synthetic anti-icing materials and low-temperature organ preservation materials. Although numerous machine learning-based methods have been proposed for AFPs prediction, the complex and diverse nature of AFPs limits the prediction performance of existing methods. In this study, we propose AFP-Deep, a new deep learning method to predict antifreeze proteins by integrating embedding from protein sequences with pre-trained protein language models and evolutionary contexts with hybrid feature extraction networks. The experimental results demonstrated that the main advantage of AFP-Deep is its utilization of pre-trained protein language models, which can extract discriminative global contextual features from protein sequences. Additionally, the hybrid deep neural networks designed for protein language models and evolutionary context feature extraction enhance the correlation between embeddings and antifreeze pattern. The performance evaluation results show that AFP-Deep achieves superior performance compared to state-of-the-art models on benchmark datasets, achieving an AUPRC of 0.724 and 0.924, respectively.

HRTBDA: a network for post-disaster building damage assessment based on remote sensing images

ABSTRACT Efficient building damage assessment after disasters is vital for emergency response and loss evaluation, but the task is complicated by diverse building structures and complex environments. Traditional methods using Convolutional Neural Networks (CNNs) struggle to capture global contextual features, limiting damage categorization accuracy. To address this, we introduce the High-Resolution Transformer Architecture for Building Damage Assessment (HRTBDA), which enhances multi-scale feature extraction. A Cross-Attention-Based Spatial Fusion (CSF) module is proposed to utilize the attention mechanism, improving the model’s ability to identify detailed associations in damaged buildings. Additionally, we propose a deep convolution network matching optimization strategy that integrates a multilayer perceptron and expands the receptive field, enhancing global feature perception. HRTBDA’s performance was evaluated on two public datasets and compared with five recent frameworks. The model achieved an F1-score of 86.0% in building localization and 78.4% in damage assessment, with a 4.8% improvement in detecting minor damages. These results demonstrate HRTBDA’s potential for improving building damage assessment and highlight its significant advancements over existing methods.

DSC-Net: Enhancing Blind Road Semantic Segmentation with Visual Sensor Using a Dual-Branch Swin-CNN Architecture.

In modern urban environments, visual sensors are crucial for enhancing the functionality of navigation systems, particularly for devices designed for visually impaired individuals. The high-resolution images captured by these sensors form the basis for understanding the surrounding environment and identifying key landmarks. However, the core challenge in the semantic segmentation of blind roads lies in the effective extraction of global context and edge features. Most existing methods rely on Convolutional Neural Networks (CNNs), whose inherent inductive biases limit their ability to capture global context and accurately detect discontinuous features such as gaps and obstructions in blind roads. To overcome these limitations, we introduce Dual-Branch Swin-CNN Net(DSC-Net), a new method that integrates the global modeling capabilities of the Swin-Transformer with the CNN-based U-Net architecture. This combination allows for the hierarchical extraction of both fine and coarse features. First, the Spatial Blending Module (SBM) mitigates blurring of target information caused by object occlusion to enhance accuracy. The hybrid attention module (HAM), embedded within the Inverted Residual Module (IRM), sharpens the detection of blind road boundaries, while the IRM improves the speed of network processing. In tests on a specialized dataset designed for blind road semantic segmentation in real-world scenarios, our method achieved an impressive mIoU of 97.72%. Additionally, it demonstrated exceptional performance on other public datasets.

GSSnowflake: Point Cloud Completion by Snowflake with Grouped Vector and Self-Positioning Point Attention

Point clouds are essential 3D data representations utilized across various disciplines, often requiring point cloud completion methods to address inherent incompleteness. Existing completion methods like SnowflakeNet only consider local attention, lacking global information of the complete shape, and tend to suffer from overfitting as the model depth increases. To address these issues, we introduced self-positioning point-based attention to better capture complete global contextual features and designed a Channel Attention module for adaptive feature adjustment within the global vector. Additionally, we implemented a vector attention grouping strategy in both the skip-transformer and self-positioning point-based attention to mitigate overfitting, improving parameter efficiency and generalization. We evaluated our method on the PCN dataset as well as the ShapeNet55/34 datasets. The experimental results show that our method achieved an average CD-L1 of 7.09 and average CD-L2 scores of 8.0, 7.8, and 14.4 on the PCN, ShapeNet55, ShapeNet34, and ShapeNet-unseen21 benchmarks, respectively. Compared to SnowflakeNet, we improved the average CD by 1.6%, 3.6%, 3.7%, and 4.6% on the corresponding benchmarks, while also reducing complexity and computational costs and accelerating training and inference speeds. Compared to other existing point cloud completion networks, our method also achieves competitive results.

Predicting transcription factor binding sites by a multi-modal representation learning method based on cross-attention network

The prediction of transcription factor binding sites (TFBS) plays a crucial role in studying cellular functions and understanding transcriptional regulatory processes. With the development of chromatin immunoprecipitation sequencing (ChIP-seq) technology, an increasing number of computer-aided TFBS prediction models have emerged. However, how to integrate multi-modal information of DNA and obtain efficient features to improve prediction accuracy remains a major challenge. Here, we propose MultiTF, a multi-modal representation learning method based on a cross-attention network for predicting transcription factor binding sites. Among TFBS prediction methods, we are the first to use graph neural networks and cross-attention networks for representation learning. MultiTF uses dna2vec to extract global contextual features of DNA sequences, DNAshapeR to extract shape features, and the CDPfold model and graph attention network for learning and representation of DNA structural features. Finally, with the help of our cross-attention module, we successfully combine sequence, structural, and shape features to achieve interactive fusion. When comparing MultiTF to other state-of-the-art methods using 165 ENCODE ChIP-seq datasets, we find that MultiTF exhibits average ACC, ROC-AUC, and PR-AUC values of 0.911, 0.978, and 0.982, respectively. The results show that MultiTF achieves unprecedented prediction accuracy compared to previous TFBS prediction models. In addition, our visual analysis of structural features provides interpretability for the prediction results.

A dual encoder crack segmentation network with Haar wavelet-based high–low frequency attention

Cracks are an earlier indication of severe structural damage, and are also an important indicator in the evaluation of structural health and monitoring processes. However, the complex background interference makes the segmentation of small cracks are an extremely challenging task. Therefore, a Dual Encoder Crack Segmentation Network (DECS-Net) based on convolutional neural network (CNN) and transformer is constructed to achieve automated crack detection. Firstly, a High–Low frequency Attention (HLA) mechanism is proposed that uses the Haar wavelet to obtain the approximate and detailed components, and further processes to obtain low-frequency and high-frequency features. In addition, a Locally Enhanced Feedforward Network (LEFN) is designed to help the network improve its local information perception ability. Secondly, a Features Fusion Module (FFM) is proposed to fuse the local features extracted by the CNN encoder and the global contextual features extracted by the transformer encoder, implemented cross-domain fusion and correlation enhancement. Finally, experiments are conducted on two public datasets DeepCrack and Crack3238, and the recall showed a remarkable improvement, with 92.70% on DeepCrack and 79.02% on Crack3238. Its comprehensive performance is ahead of the ten state-of-the-art models.

DSC-Net: learning discriminative spatial contextual features for semantic segmentation of large-scale ancient architecture point clouds

Semantic segmentation of point cloud data of architectural cultural heritage is of significant importance for HBIM modeling, disease extraction and analysis, and heritage restoration research fields. In the semantic segmentation task of architectural point cloud data, especially for the protection and analysis of architectural cultural heritage, the previous deep learning methods have poor segmentation effects due to the complexity and unevenness of the data, the high geometric feature similarity between different components, and the large scale changes. To this end, this paper proposes a novel encoder-decoder architecture called DSC-Net. It consists of an encoder-decoder structure based on point random sampling and several fully connected layers for semantic segmentation. To overcome the loss of key features caused by random downsampling, DSC-Net has developed two new feature aggregation schemes: the enhanced dual attention pooling module and the global context feature module, to learn discriminative features for the challenging scenes mentioned above. The former fully considers the topology and semantic similarity of neighboring points, generating attention features that can distinguish categories with similar structures. The latter uses spatial location and neighboring volume ratio to provide an overall view of different types of architectural scenes, helping the network understand the spatial relationships and hierarchical structures between different architectural elements. The proposed modules can be easily embedded into various network architectures for point cloud semantic segmentation. We conducted experiments on multiple datasets, including the ancient architecture dataset, the ArCH architectural cultural heritage dataset, and the publicly available architectural segmentation dataset S3DIS. The results show that the mIoU reached 63.56%, 55.84%, and 71.03% respectively. The experimental results prove that our method has the best segmentation effect in dealing with challenging architectural cultural heritage data and also demonstrates its practicality in a wider range of architectural point cloud segmentation applications.

An offset-transformer hierarchical model for point cloud-based resistance spot welding quality classification

Resistance spot welding (RSW) is a widely used welding technology in automotive manufacturing, and weld nugget quality is closely related to the quality of the vehicle body. Offline random checks are largely relied on the quality inspection of weld nuggets, but they have low efficiency and high cost. To address this issue, this paper proposes a deep learning model for RSW weld nugget classification, named the offset-transformer hierarchical model (OFTFHC), which is based on the point cloud data of its appearance shape. OFTFHC uses a hierarchical network structure to gradually expand the receptive field. A local feature module is introduced to extract local features from the point cloud, effectively enabling the recognition of the fine structural features of the resistance spot weld point cloud. A residual ratio module, which is based on MLP_MA and uses max and average functions for feature enhancement, is designed to adapt to the complex spatial structure of the point cloud. The offset-transformer structure is used to learn global context features, thereby enhancing the global feature extraction capability. Through classification experiments on RSW weld nuggets across 5 categories with a total of 1050 samples, OFTFHC achieved an average accuracy of 80.6 %, outperforming existing models. This demonstrates the effectiveness and superiority of the method, making it highly suitable for weld nugget quality control in automotive automation production lines.

NLA-GCL-Net: semantic segmentation of large-scale surveying point clouds based on neighborhood label aggregation (NLA) and global context learning (GCL)

For large-scale 3D point clouds from surveying, the RandLA-Net semantic segmentation network model is unable to learn global context features efficiently during training, leading to suboptimal feature acquisition of globally related features. Furthermore, inconsistent predictions are made in the vicinity of data boundaries due to the oversimplified usage of multi-layer perceptrons (MLPs) and non-linear activation functions following decoding, which has a detrimental impact on both model training and performance. This study provides two solutions to address these issues: a neighborhood label aggregation (NLA) classifier that aggregates neighborhood information for segmentation tasks, and a global context learning (GCL) module that learns global volume-relative information to improve the semantic segmentation network. Overall accuracy (OA) obtained via experimental validation on large-scale SensatUrban and S3DIS datasets is 91.80% and 88.8%, respectively. The proposed model outperforms RandLA-Net by 3.8% and 3.0% in overall mean Intersection over Union (mIOU), respectively, significantly improving model performance and generalization abilities. This work offers new perspectives on how to effectively segment point cloud data.

An Infrared Aircraft Detection Algorithm Based on Context Perception Feature Enhancement

To address the issue of insufficient extraction of target features and the resulting impact on detection performance in long-range infrared aircraft target detection caused by small imaging area and weak radiation intensity starting from the idea of perceiving target context to enhance the features extracted by convolutional neural network, this paper proposes a detecting algorithm based on AWFGLC (adaptive weighted fusion of global–local context). Based on the mechanism of AWFGLC, the input feature map is randomly reorganized and partitioned along the channel dimension, resulting in two feature maps. One feature map is utilized by self-attention for global context modeling, establishing the correlation between target features and background features to highlight the salient features of the target, thereby enabling the detecting algorithm to better perceive the global features of the target. The other feature map is subjected to window partitioning, with max pooling and average pooling performed within each window to highlight the local features of the target. Subsequently, self-attention is applied to the pooled feature map for local context modeling, establishing the correlation between the target and its surrounding neighborhood, further enhancing the weaker parts of the target features, and enabling the detecting algorithm to better perceive the local features of the target. Based on the characteristics of the target, an adaptive weighted fusion strategy with learnable parameters is employed to aggregate the global context and local context feature maps. This results in a feature map containing more complete target information, enhancing the ability of the detection algorithm to distinguish between target and background. Finally, this paper integrates the mechanism of AWFGLC into YOLOv7 for the detection of infrared aircraft targets. The experiments indicate that the proposed algorithm achieves mAP50 scores of 97.8% and 88.7% on self-made and publicly available infrared aircraft datasets, respectively. Moreover, the mAP50:95 scores reach 65.7% and 61.2%, respectively. These results outperform those of classical target detection algorithms, indicating the effective realization of infrared aircraft target detection.

GCPDFFNet: Small Object Detection for Rice Blast Recognition.

Early detection of rice blast disease is pivotal to ensure rice yield. We collected in situ images of rice blast and constructed a rice blast dataset based on variations in lesion shape, size, and color. Given that rice blast lesions are small and typically exhibit round, oval, and fusiform shapes, we proposed a small object detection model named GCPDFFNet (global context-based parallel differentiation feature fusion network) for rice blast recognition. The GCPDFFNet model has three global context feature extraction modules and two parallel differentiation feature fusion modules. The global context modules are employed to focus on the lesion areas; the parallel differentiation feature fusion modules are used to enhance the recognition effect of small-sized lesions. In addition, we proposed the SCYLLA normalized Wasserstein distance loss function, specifically designed to accelerate model convergence and improve the detection accuracy of rice blast disease. Comparative experiments were conducted on the rice blast dataset to evaluate the performance of the model. The proposed GCPDFFNet model outperformed the baseline network CenterNet, with a significant increase in mean average precision from 83.6 to 95.4% on the rice blast test set while maintaining a satisfactory frames per second drop from 147.9 to 122.1. Our results suggest that the GCPDFFNet model can accurately detect in situ rice blast disease while ensuring the inference speed meets the real-time requirements.

Stereo Image Restoration via Attention-Guided Correspondence Learning.

Although stereo image restoration has been extensively studied, most existing work focuses on restoring stereo images with limited horizontal parallax due to the binocular symmetry constraint. Stereo images with unlimited parallax (e.g., large ranges and asymmetrical types) are more challenging in real-world applications and have rarely been explored so far. To restore high-quality stereo images with unlimited parallax, this paper proposes an attention-guided correspondence learning method, which learns both self- and cross-views feature correspondence guided by parallax and omnidirectional attention. To learn cross-view feature correspondence, a Selective Parallax Attention Module (SPAM) is proposed to interact with cross-view features under the guidance of parallax attention that adaptively selects receptive fields for different parallax ranges. Furthermore, to handle asymmetrical parallax, we propose a Non-local Omnidirectional Attention Module (NOAM) to learn the non-local correlation of both self- and cross-view contexts, which guides the aggregation of global contextual features. Finally, we propose an Attention-guided Correspondence Learning Restoration Network (ACLRNet) upon SPAMs and NOAMs to restore stereo images by associating the features of two views based on the learned correspondence. Extensive experiments on five benchmark datasets demonstrate the effectiveness and generalization of the proposed method on three stereo image restoration tasks including super-resolution, denoising, and compression artifact reduction.

LACTNet: A Lightweight Real-Time Semantic Segmentation Network Based on an Aggregated Convolutional Neural Network and Transformer

Transformers have demonstrated a significant advantage over CNNs in modeling long-range dependencies, leading to increasing attention being paid towards their application in semantic segmentation tasks. In the present work, a novel semantic segmentation model, LACTNet, is introduced, which synergistically combines Transformer and CNN architectures for the real-time processing of local and global contextual features. LACTNet is designed with a lightweight Transformer, which integrates a specially designed gated convolutional feedforward network, to establish feature dependencies across distant regions. A Lightweight Average Feature Bottleneck (LAFB) module is designed to effectively capture spatial detail information within the features, thereby enhancing segmentation accuracy. To address the issue of spatial feature loss in the decoder, a long skip-connection approach is employed through the designed Feature Fusion Enhancement Module (FFEM), which enhances the integrity of spatial features and the feature interaction capability in the decoder. LACTNet is evaluated on two datasets, achieving a segmentation accuracy of 74.8% mIoU and a frame rate of 90 FPS on the Cityscapes dataset, and a segmentation accuracy of 71.8% mIoU with a frame rate of 126 FPS on the CamVid dataset.

A novel plant type, leaf disease and severity identification framework using CNN and transformer with multi-label method.

The growth of plants is threatened by numerous diseases. Accurate and timely identification of these diseases is crucial to prevent disease spreading. Many deep learning-based methods have been proposed for identifying leaf diseases. However, these methods often combine plant, leaf disease, and severity into one category or treat them separately, resulting in a large number of categories or complex network structures. Given this, this paper proposes a novel leaf disease identification network (LDI-NET) using a multi-label method. It is quite special because it can identify plant type, leaf disease and severity simultaneously using a single straightforward branch model without increasing the number of categories and avoiding extra branches. It consists of three modules, i.e., a feature tokenizer module, a token encoder module and a multi-label decoder module. The LDI-NET works as follows: Firstly, the feature tokenizer module is designed to enhance the capability of extracting local and long-range global contextual features by leveraging the strengths of convolutional neural networks and transformers. Secondly, the token encoder module is utilized to obtain context-rich tokens that can establish relationships among the plant, leaf disease and severity. Thirdly, the multi-label decoder module combined with a residual structure is utilized to fuse shallow and deep contextual features for better utilization of different-level features. This allows the identification of plant type, leaf disease, and severity simultaneously. Experiments show that the proposed LDI-NET outperforms the prevalent methods using the publicly available AI challenger 2018 dataset.

MFEFNet: A Multi-Scale Feature Information Extraction and Fusion Network for Multi-Scale Object Detection in UAV Aerial Images

Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively.