Recent research has extensively investigated working memory (WM)-guided attention, which is the phenomenon of attention being directed towards information in the external environment that matches the content stored in WM. While prior studies have foc...
Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and pr...
Working memory and attention are interrelated constructs that are sometimes even considered indistinguishable. Since attention is not a uniform construct, it is possible that different types of attention affect working memory capacity differently. To...
Recently, it has been demonstrated that the performance of an object detection network can be improved by embedding an attention module into it. In this work, we propose a lightweight and effective attention mechanism named multibranch attention (M3A...
Image captioning with a natural language has been an emerging trend. However, the social image, associated with a set of user-contributed tags, has been rarely investigated for a similar task. The user-contributed tags, which could reflect the user a...
Background/Objectives: While deficits in executive attention and alerting systems in children with attention deficit hyperactivity disorder (ADHD) are well-documented, findings regarding orienting attention in ADHD have been inconsistent. The current...
A substantial question in understanding expert behavior is isolating where experts look, and which aspects of their environment they process. While tracking the position of gaze provides some insight into this process, our ability to attend covertly...
The attention mechanism is essential to convolutional neural network (CNN) vision backbones used for sensing and imaging systems. Conventional attention modules are designed heuristically, relying heavily on empirical tuning. To tackle the challenge...
Attention is crucial as a fundamental prerequisite for perception. The measurement of attention in viewing and recognizing the images that surround us constitutes an important part of eye movement research, particularly in advertising-effectiveness r...
The current intelligent driving system does not consider the selective attention mechanism of drivers, and it cannot completely replace the drivers to extract effective road information. A Driver Visual Attention Network (DVAN), which is based on dee...
The Unmanned Aerial Vehicle (UAV) tracking tasks find extensive utility across various applications. However, current Transformer-based trackers are generally tailored for diverse scenarios and lack specific designs for UAV applications. Moreover, du...
Existing CNN-based super resolution methods have low emphasis on high-frequency features, resulting in poor performance for contours and textures. To solve this problem, this paper proposes single image super resolution using an attention mechanism t...
In her aim to establish education as a scientifically grounded discipline—conceived as “an experimental science” in her non-fictional treatise Practical Education (1798)—Maria Edgeworth pioneered the integration of literary at...
The incorporation of attention mechanisms into hypergraphs is an effective method for obtaining appropriate node and hyperedge representations. However, the ability of attention mechanisms to aggregate features on a hypergraph can be improved, partic...
This paper presents a novel facial expression recognition network, called Distract your Attention Network (DAN). Our method is based on two key observations in biological visual perception. Firstly, multiple facial expression classes share inherently...
Linear attention has gained popularity in recent years due to its lower computational complexity compared to Softmax attention. However, its relatively lower performance has limited its widespread application. To address this issue, we propose a plug...
Attention, referring to selective processing of task-related information, is central to cognition. It has been proposed that voluntary attention (driven by current goals or tasks and under top-down control) and reflexive attention (driven by stimulus...
The complexity of polyphonic sounds imposes numerous challenges on their classification. Especially in real life, polyphonic sound events have discontinuity and unstable time-frequency variations. Traditional single acoustic features cannot character...
Convolutional neural networks have demonstrated excellent performance in oral cancer detection and classification. However, the end-to-end learning strategy makes CNNs hard to interpret, and it can be challenging to fully understand the decision-maki...
As demonstrated in hybrid connectionist temporal classification (CTC)/Attention architecture, joint training with a CTC objective is very effective to solve the misalignment problem existing in the attention-based end-to-end automatic speech recognit...
To efficiently use its finite resources, the visual system selects for further processing only a subset of the rich sensory information. Visual masking and spatial attention control the information transfer from visual sensory-memory to visual short-...
In this paper, I discuss attention in terms of selecting visual information and acting on it. Selection has been taken as a bedrock concept in attention research since James (1890). Selective attention guides action by privileging some things at the...
The automated analysis of pool game videos presents significant challenges due to complex object interactions, precise rule requirements, and event-driven game dynamics that traditional computer vision approaches struggle to address effectively. This...
In this paper, I first review signal detection theory (SDT) approaches to perception, and then discuss why it is thought that SDT theory implies that increasing attention improves performance. Our experiments have shown, however, that this is not nec...
(1) Background: The purpose of this study was to investigate the effectiveness of a small group intervention program named “Attentive Kindergarten” (AK), aimed at enhancing attention functioning among typically developing preschool-aged c...
Attention is an essential psychological component in sports games, which conditions sports success. The purpose of this study was to identify the attention style (internal or external) and the weight of attention qualities depending on the practiced...
The purpose of aspect-based sentiment classification is to identify the sentiment polarity of each aspect in a sentence. Recently, due to the introduction of Graph Convolutional Networks (GCN), more and more studies have used sentence structure infor...
Medical image segmentation is a critical task in image analysis and plays an essential role in computer-aided diagnosis. Despite the promising performance of hybrid models combining U-Net and transformer architectures, these approaches face challenge...
Spiking neural networks (SNNs), a neural network model structure inspired by the human brain, have emerged as a more energy-efficient deep learning paradigm due to their unique spike-based transmission and event-driven characteristics. Combining SNNs...
This article provides an introduction to experimental research on top-down human attention in complex scenes, written for cognitive scientists in general. We emphasize the major effects of goals and intention on mental function, measured with behavio...
Research indicates that music can influence human cognitive functions. Diverse musical settings can affect alertness, orientation, and executive control of attention in various populations. Exploring the relationship between designers with highly cre...
In today’s fast paced, attention-demanding society, executive functions and attentional resources are often taxed. Individuals need ways to sustain and restore these resources. We first review the concepts of attention and restoration, as insta...
The disaster of the COVID-19 pandemic has claimed numerous lives and wreaked havoc on the entire world due to its transmissible nature. One of the complications of COVID-19 is pneumonia. Different radiography methods, particularly computed tomography...
Reward has been shown to influence selective attention, yet previous research has primarily focused on rewards associated with specific locations or features, with limited investigation into the impact of a reward object on object-based attention (OB...
Low-light image enhancement is a crucial preprocessing task in complex vision tasks. It directly impacts object detection, image segmentation, and image recognition outcomes. In recent years, with the continuous development of deep learning technique...
This review explores the pivotal role of attention in everyday life, emphasizing the significance of studying attention-related brain functions. We delve into the development of methodologies for investigating attention and highlight the crucial role...
Crowd Crowd counting is not simply a matter of counting the numbers of people, but also requires that one obtains people’s spatial distribution in a picture. It is still a challenging task for crowded scenes, occlusion, and scale variation. Thi...
The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional net...
Deep learning models and graphics processing units have completely transformed the field of machine learning. Recurrent neural networks and long short-term memories have been successfully used to model and predict complex systems. However, these clas...
Twitter may be considered to be a decentralized social information processing platform whose users constantly receive their followees’ information feeds, which they may in turn dispatch to their followers. This decentralization is not devoid of...
The task of pitch estimation is an essential step in many audio signal processing applications. In this paper, we propose a data-driven pitch estimation network, the Dual Attention Network (DA-Net), which processes directly on the time-domain samples...
Attention biases to stimuli with emotional content may play a role in the development and maintenance of anxiety disorders. The most commonly used tasks in measuring and treating such biases, the dot-probe and spatial cueing tasks, have yielded mixed...
In this paper, we provide external image features and use the internal attention mechanism to solve the VQA problem given a dataset of textual questions and related images. Most previous models for VQA use a pair of images and questions as input. In...
15 November 2022
Recent developments in brain–computer interface (BCI) technology have shown great potential in terms of estimating users’ mental state and supporting children’s attention training. However, existing training tasks are relatively sim...
Domain adaptation is critical to transfer the invaluable source domain knowledge to the target domain. In this paper, for a particular visual attention model, saying hard attention, we consider to adapt the learned hard attention to the unlabeled tar...
Segmentation of skin lesions is a challenging task because of the wide range of skin lesion shapes, sizes, colors, and texture types. In the past few years, deep learning networks such as U-Net have been successfully applied to medical image segmenta...
Theoretical models of sleep and attention deficit hyperactivity disorder (ADHD) suggest that symptoms of ADHD are associated with daytime sleepiness, but it has received little support. The present study aimed at testing an alternative model involvin...
Photovoltaic (PV) fault detection faces significant challenges in distinguishing subtle defects from complex backgrounds while maintaining reliability across diverse environmental conditions. Traditional approaches struggle with scalability and accur...
Automatic music transcription (AMT) aims to convert raw audio signals into symbolic music. This is a highly challenging task in the fields of signal processing and artificial intelligence, and it holds significant application value in music informati...
Attention deficit hyperactivity disorder is characterized by several cognitive and behavioral problems such as inattention and impulsivity, abnormal control of eye movements and relocation, visual fixation and visuospatial perception. There is a link...
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