Accurate medical image segmentation is essential for physicians to obtain high-quality diagnostic results. While recent hybrid Transformer and CNN based methods have significantly improved the performance of biomedical image segmentation to meet ...${}^{\mathrm{}}$${}^{\mathrm{}}$

Utilizing deep neural networks for automatic segmentation of skin lesion images represents a significant advancement in current research. The issue of class imbalance poses a major challenge in most skin lesion datasets, as skin cancer patients ...

In Vehicular Edge Computing (VEC), Unmanned Aerial Vehicles (UAVs) have become a feasible solution for addressing high deployment costs faced by base stations in congested roads during peak hours. However, UAVs cannot cache all requested content due to ...

The development of novel applications causes increased demands on the computational capabilities of Vehicular Edge Computing (VEC). Current works have introduced Unmanned Aerial Vehicles (UAVs) into VEC to solve the resource-constrained problem. However, ...

• A novel approach to intelligently analyze litchi fruit abscission of branches in the orchard.
• A fixed-point and fixed-focus data acquisition system was developed.
• The improved SuperGlue network was employed to match branches and ...

Automatic tracking of litchi fruit abscission provides important information for the orchard management. Currently, the prevalent method for tracking litchi fruit abscission involves manual selection of branches followed by daily fruit counting, ...

Multimodal learning has made great achievements in recent years. However, most of the existing modal fusion methods capture cross-modal correlations in a single stage and are designed for specific tasks, with poor generalizability and ...

• An universal multimodal fusion method is proposed.
• Modality Transfer and Fusion Refinement are used in our method.
• The method has perfect cross-modal learning capabilities and generalizability.
• The method achieves better ...

Automatic skin lesion segmentation from dermoscopy images is of great significance in the early treatment of skin cancers, which is yet challenging even for dermatologists due to the inherent issues, i.e., considerable size, shape and color ...

Compared to generalized object detection, research on small object detection has been slow, mainly due to the need to learn appropriate features from limited information about small objects. This is coupled with difficulties such as information ...

Medical image segmentation plays a pivotal role in computer-assisted medical diagnosis, contributing to precise diagnostics, treatment strategizing, and disease tracking. However, the availability of annotated data for medical image segmentation ...

Detecting objects in aerial images poses a challenging task due to the presence of numerous small objects and complex environmental information. To address these problems, we propose an efficient detector specifically designed for aerial images, ...

• An environment-adaptive object detection is proposed for small object detection in complex environments.
• Quickly capture more contextual information while leveraging the redundancy of channel information.
• Utilizing complex ...

Skin cancer is one of the most serious threats to human health among skin lesions. Computer-aided diagnosis methods can assist patients in identifying and detecting skin lesion types early, thereby enabling corresponding treatments. In this paper,...

Peripheral vision is a vital component of human visual processing that allows for efficient and accurate recognition of visual features across diverse regions of the visual field. Analogously, endoscopic images often exhibit peripheral regions of ...

• Self-Peripheral-Attention blends peripheral vision with self-attention.
• Central detail features and multi-level peripheral features adaptively fused.
• SPA-Net’s performance validated with four endoscopic datasets.
• Peripheral ...

In this paper, we study multimodal metaphor detection to obtain real semantic meaning from multiple heterogeneous information sources. The existing approaches mainly suffer from two drawbacks. (1) They focus on textual aspects, overlooking the ...

Semi-supervised learning (SSL) provides methods to improve model performance through unlabeled samples. In medical image analysis, the challenges of multi-category classification and imbalance learning must be addressed effectively. Pseudo ...

• Novel semi-supervised medical image classification framework with three loss functions proposed.
• Growth Threshold for Pseudo Labeling introduced, improving utilization of unlabeled samples.
• Pseudo Label Dropout proposed to ...

Accurate segmentation of target areas in medical images, such as lesions, is essential for disease diagnosis and clinical analysis. In recent years, deep learning methods have been intensively researched and have generated significant progress in ...

• We propose a novel coupled refinement and focus exploration network, CRFENet.
• CRM enables decoupled and targeted optimization of low and high frequency features.
• MEFM performs a two-stage exploration and fusion using our proposed ...

The Transformer architecture has been widely applied in the field of image segmentation due to its powerful ability to capture long-range dependencies. However, its ability to capture local features is relatively weak and it requires a large ...

• An efficient asymmetric CNN and Transformer parallel framework.
• A high-density information fusion strategy with a 0.05M-parameter fusion module.
• Customized multi-composite loss function optimized training process.
• Adopts low-...

Event extraction (EE) is an important natural language processing task. With the passage of time, many powerful and effective models for event extraction tasks have been developed. However, there has been limited research on complex overlapping event ...

Recently, three-dimensional (3D) point-cloud analysis has been extensively utilized in the domain of machine vision, encompassing tasks include shape classification and segmentation. However the inherent disorder in point clouds poses a challenge in ...

Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. ...

In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object ...