Neurocomputing

This study investigates the coverage extendibility of generalized prediction error estimation for the random vector functional link (RVFL) network and generalized extreme learning machine (GELM). GELM is an ELM applying the limiting behavior of ...

Understanding how neural networks generalize on unseen data is crucial for designing more robust and reliable models. In this paper, we study the generalization gap of neural networks using methods from topological data analysis. For this purpose,...

This paper investigates the consensus problem of a class of high-dimensional nonlinear multi-agent systems. The agents are homogeneous and have coupled interactions with their nearby agents. Firstly, we study the consensus of this class of ...

Motor imagery (MI) brain-computer interface (BCI) paradigms have been extensively used in neurological rehabilitation. However, due to the required long calibration time and non-stationary nature of electroencephalogram (EEG) signals, it is ...

Multi-view clustering integrates features from different views to perform clustering. This problem has attracted increasing attention in recent years because multi-view data has become more common. The mainstream methods focus on learning a ...

Multi-exposure image fusion (MEF) aims to combine multiple images with different exposures into a single image to improve visual quality and preserve details. This paper proposes a curve embedding network for MEF (CurveMEF), which formulates the ...

As an important branch of emerging artificial intelligence algorithms, multi-agent reinforcement learning (MARL) has shown strong performance in collaborative environments. It can utilize multiple agents to find the optimal set of strategies for ...

Display Omitted

Simultaneously enhancing the visual effects and resolution of underwater images poses a challenging task as it involves two types of image enhancement tasks, underwater image enhancement and image super-resolution. In spite of the emergence of ...

The DEtection TRansformer (DETR), with its elegant architecture and set prediction, has revolutionized object detection. However, DETR-like models have yet to achieve comparable success in temporal action detection (TAD). To address this gap, we ...

• Query-based temporal action detection with streamlined processing.
• Breakthrough: Surpassing 70% precision on THUMOS14.
• Deep insights gleaned from expansive experimentation.

CLIP4Clip model transferred from the CLIP has been the de-factor standard to solve the video clip retrieval task from frame-level input, triggering the surge of CLIP4Clip-based models in the video–text retrieval domain. In this work, we rethink ...

After detecting different external light stimuli, photosensitive neurons encode these stimuli and trigger different discharge patterns and membrane potentials, thereby transmitting signals in the neural network. The cross-ring structure can ...

Recently, the rapid progress of artificial intelligence has enhanced the human-robot relationship through the development of several autonomous robots; such as drones. The overwhelming rise of drones has brought both relevant advantages and ...

Local Linear Embedding (LLE) is a nonlinear manifold learning method for dimensionality reduction in high-dimensional data. However, when the data is distorted by noise, efficiency of LLE significantly diminishes. This paper proposes a robust ...

Image generation from a single natural image using generative adversarial networks (GANs) has attracted extensive attention recently due to the GANs’ practical ability to produce photo-realistic images and their potential applications in computer ...

With the rapid development and wide application of neural networks in various domains including safety-critical systems, it is more and more important to investigate formal methods to provide strict guarantees on their behavior. As formal ...

This paper primarily examines group consensus (GC) in heterogeneous multi-agent systems (HMASs) within the context of cooperative-competitive networks and input delay. The agent dynamics are modeled using single and double integrators, while the ...

This paper proposes a learning-based hyper-heuristic algorithm for the coverage path planning problem of multiple unmanned surface vehicles (USV). The makespan and coverage of the USVs are considered simultaneously. The proposed method does not ...

• Triangular fuzzy numbers are used to represent the mapping time and mapping radius.
• A multi-layer encoding is designed to optimize makespan and coverage of multiple USVs.
• A local search operator based on shortest distance ...

In this paper, a distributed control algorithm based on a data-driven approach is developed to solve the consensus control of heterogeneous nonlinear Multi-Agent Systems (MAS). The consensus obtained from the solution of the Hamilton–Jacobi–...

Brain age estimation involves predicting an individual’s biological age from their brain images. This process offers valuable insights into the aging process and the progression of neurodegenerative diseases. Conducting large-scale datasets for ...

• The dual-stream greedy method effectively addresses limitations in brain age estimation, achieving robust performance with limited datasets.
• It effectively utilizes both local and global aspects of the brain, demonstrating remarkable ...

Adversarial robustness is one of the most challenging problems in Deep Learning and Computer Vision research. State-of-the-art techniques to enforce robustness are based on Adversarial Training, a computationally costly optimization procedure. ...

Machine learning and deep learning models are increasingly susceptible to adversarial attacks, particularly in critical areas like cybersecurity and Information Disorder. This study provides a comprehensive evaluation of model Robustness against ...

Depth estimation can provide auxiliary information for scene perception. Generally, extensive textureless surfaces, such as walls and ceilings, exist in indoor environments, and they share similar scene and semantic content. Overly consistent ...

Path planning is a key process in robotics, playing an important role in fields such as autonomous driving and logistic delivery. Our work addresses the dual challenges of training efficiency and composite optimization in path planning using Deep ...

Neural networks in object detection methods are designed to handle the challenging task of multi-scale object detection in computer vision. However, small objects occupy small blocks of pixels, and feature information can easily be overwhelmed, ...

Interpreting style transfer methods and generating high-quality style images are two challenging computer vision tasks. However, most of the current image style transfer methods are inexplicable, and their image cartoonilation performance is also ...

While Graph Contrastive Learning (GCL) has demonstrated satisfactory outcomes, three main problems remain. First, to effectively transmit the message of higher-order hop nodes, it is imperative to stack multiple graph convolution layers, which ...

The digitization of variety identification is of great importance for the improvement of farming practices in date fruit production. In this study, we have developed a hybrid approach called DenseViT-XGB for date fruit variety identification. ...

Semantic segmentation has been one of the most critical tasks in computer vision. Recent works mainly focus on improving segmentation performance by designing high-capacity transformer architectures. They try to solve the high data consumption ...

• Context-aware Fourier Transform is proposed to replace self-attention for segmentation.
• Hierarchical Fourier Transform fuses the token-channel contexts to reduce computation cost.
• Adaptive Modulation Unit fuses the spatial-...

Credit risk classification is a crucial area in machine learning-enhanced financial decision support systems, and numerous studies have achieved significant progress. However, data in the modern financial landscape is inherently complex, ...

• An SHDA method is proposed for the few-sample CRC with few labels and class imbalance.
• The SHDA method can solve the three primary problems in CRC using only one model.
• An imbalanced data augmentation method is suggested to enhance ...