Search Results

Self-training has been shown to achieve remarkable gains in semi-supervised semantic segmentation by creating pseudo-labels using unlabeled data. This approach, however, suffers from the quality of the generated pseudo-labels, and generating higher ...

This paper proposes an improved crowding distance based non-dominated sorting genetic algorithm II, called INSGAII, to solve the three-dimensional collaborative path planning problem for unmanned combat aerial vehicles (UCAVs) with dynamic ...

The development of the vehicular ad-hoc network (VANET) provides a promising solution to promoting road safety and driving experiences, but it also generates massive data, leading to network configuration and management issues. Fortunately, by integrating ...

Image captioning aims to generate a description of a given image. However, inherent representation differences between images and sentences make it difficult to align semantic meanings for captioning. Inspired by the human cognitive processes of ...

Software-defined networking (SDN) has significantly enriched network functions by separating the control plane from the data plane. Meanwhile, the unique architecture of SDN brings new security challenges. Recent studies show that attackers can fabricate ...

Utilizing functional magnetic resonance imaging (fMRI) to model functional brain networks (FBNs) is increasingly prominent in attention-deficit/hyperactivity disorder (ADHD) research, revealing neural impact and mechanisms through the exploration ...

• Build an integrated framework with modeling, multimodal fusion, and classification.
• Construct FBNs as ADHD biomarkers by capturing regional correlations and LDDs.
• Integrate domain knowledge with FBNs for intra-and inter-modal ...

Software-Defined Networking (SDN) significantly simplifies the management of networks by deploying various applications. However, the performance gap between the application and the forwarding device brings new security concerns for the network. In this ...

Remote sensing image scene classification has made great progress with deep learning. Due to complex backgrounds and the large number of objects with inhomogeneous sizes, remote sensing image scene classification still remains challenging. In ...

The layout optimization of wind turbines seeks to improve wind power conversion efficiency by minimizing the wake effect in wind farm. However, the existing optimization methods cannot provide a layout with high output power due to their inability ...

Accurate segmentation of the adrenal gland from abdominal computed tomography (CT) scans is a crucial step towards facilitating the computer-aided diagnosis of adrenal-related diseases such as essential hypertension and adrenal tumors. However, ...

• We propose MCNet for the segmentation of adrenal glands in CT images.
• MCNet can better integrate multi-level features to accurately segment adrenal glands.
• We evaluate the effectiveness of MCNet on both private and public datasets.

Efficient and accurate medical image segmentation is necessary for pathological evaluation and disease diagnosis in clinical practice. In recent years, the U-shaped encoder–decoder structure has achieved good performance in various medical image ...

• We propose a novel framework called C3-Net for medical image segmentation tasks.
• Our C3-Net address the inherent semantic gap and global semantic dilution problems.
• We evaluate the effectiveness of C3-Net on four public and private ...

RGB-D salient object detection (SOD) focuses on utilizing the complementary cues of RGB and depth modalities to detect and segment salient regions. However, many proposed methods train their models in a simple multi-modal manner, ignoring the differences ...

Neural architecture search (NAS) has been introduced into the design of deep neural network architectures for Magnetic Resonance Imaging (MRI) reconstruction since NAS-based methods can acquire the complex network architecture automatically ...

• Parameterized feature learning modules are added to the search space.
• The hierarchical search is proposed to alleviate the increasing searching time.
• Lightweight operations are utilized in global and local feature learning modules.

Network intrusion detection plays a very important role in network security. Although current deep learning-based intrusion detection algorithms have achieved good detection performance, there are still limitations in dealing with unbalanced ...

In clinical practice, automatic polyp segmentation in colonoscopy images is important for computer-aided clinical diagnosis of colorectal cancer. Existing polyp segmentation methods still suffer from the challenges of false positive/negative ...

• We propose a novel automatic method called DSNet for polyp segmentation.
• Our DSNet can extract and remove both false positive and false negative distractions.
• We evaluate the effectiveness of the proposed method on six public ...

Increasing attention has been paid to attention-deficit hyperactivity disorder (ADHD)-assisted diagnosis using functional brain networks (FBNs) since FBNs-based ADHD diagnosis can not only extract the functional connectivities from FBNs as potential ...

Spatio-temporal modelling based on resting-state functional magnetic resonance imaging (rsfMRI) of ADHD has been a major concern in the neuroimaging community, given the differences in the role of brain regions between attention deficit hyperactivity ...

Abundant spectral information of hyperspectral images (HSI) provides rich information for HSI classification, which often brings high dimensional data resulting in the dilemma between the demand for fine data and the limited resources ...

Image captioning aims to describe the content of an image with a complete and natural sentence. Recently, the image captioning methods with encoder-decoder architecture has made great progress, in which LSTM became a dominant decoder to generate ...

The spatiotemporal representation of task-based brain functional networks is a key topic in functional magnetic resonance image (fMRI) research. At present, deep learning has been more powerful and flexible in brain functional network research than ...