Spatial Transformer Networks are considered a powerful algorithm to learn the main areas of an image, but still, they could be more efficient by receiving images with embedded expert knowledge. This paper aims to improve the performance of convention...
Cultural heritage spatial patterns reflect both the long-term accumulation of historical resources and their successive recognition within national heritage protection systems. Here, the recognition year specifically refers to the time when a heritag...
Molecular recognition based on non-covalent interactions between two or more molecules plays a crucial role in biological systems. Specific biological molecule recognition has been widely applied in biotechnology, clinical diagnosis, and treatment. T...
Recently, extensive research efforts have concentrated on comprehending the semantic features of images in the field of computer vision. In order to address the spatial orientation relations among water surface targets (WSTs) in an image, which is a...
The recognition of point patterns in spatial vector data has important applications in geographic mapping and formation recognition. However, the application of traditional methods to spatial vector data faces two difficulties. Firstly, these data ar...
Behavior analysis of wild felines has significance for the protection of a grassland ecological environment. Compared with human action recognition, fewer researchers have focused on feline behavior analysis. This paper proposes a novel two-stream ar...
Occlusion or posture change of the face in natural scenes has typical asymmetry; however, an asymmetric face plays a key part in the lack of information available for facial expression recognition. To solve the problem of low accuracy of asymmetric f...
Periocular recognition has emerged as a particularly valuable biometric identification method in challenging scenarios, such as partially occluded faces due to COVID-19 protective masks masks, in which face recognition might not be applicable. This w...
Focusing on the issue of the low recognition rates achieved by traditional deep-information-based action recognition algorithms, an action recognition approach was developed based on skeleton spatial–temporal and dynamic features combined with...
Gait recognition is a distinctive biometric technique that can identify pedestrians by their walking patterns from considerable distances. A critical challenge in gait recognition lies in effectively acquiring discriminative spatial-temporal represen...
The proposed method has 30 streams, i.e., 15 spatial streams and 15 temporal streams. Each spatial stream corresponds to each temporal stream. Therefore, this work correlates with the symmetry concept. It is a difficult task to classify video-based f...
Although graph convolutional networks (GCNs) have shown their demonstrated ability in skeleton-based action recognition, both the spatial and the temporal connections rely too much on the predefined skeleton graph, which imposes a fixed prior knowled...
Targeting the problems of the insufficient utilization of temporal and spatial information in videos and a lower accuracy rate, this paper proposes a human action recognition method for dynamic videos of emergency rescue based on a spatial-temporal f...
A very challenging task for action recognition concerns how to effectively extract and utilize the temporal and spatial information of video (especially temporal information). To date, many researchers have proposed various spatial-temporal convoluti...
Electroencephalography (EEG)-based emotion recognition is an important biosensing technique for affective brain-computer interfaces (BCIs), mental-state assessment, and physiological monitoring. Existing methods often rely on a single spectral descri...
Skeleton representation learning offers substantial advantages for action recognition by encoding intricate motion details and spatial–temporal dependencies among joints. However, fully supervised approaches necessitate large amounts of annotat...
In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-si...
Brain biometrics have received increasing attention from the scientific community due to their unique properties compared to traditional biometric methods. Many studies have shown that EEG features are distinct across individuals. In this study, we p...
This paper proposes a facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model effectively balances model complexity and recognition accuracy. It has two key components: a lightweight feat...
Museum fatigue can weaken visitor experience and learning outcomes, yet post-visit questionnaires and controlled experiments often miss early, low-intensity behavioral changes during natural visits. This study proposes a Micro-behavioral Load Decodin...
Action recognition in robotics is a research field that has gained momentum in recent years. In this work, a video activity recognition method is presented, which has the ultimate goal of endowing a robot with action recognition capabilities for a mo...
Recently, automatic modulation recognition has been an important research topic in wireless communication. Due to the application of deep learning, it is prospective of using convolution neural networks on raw in-phase and quadrature signals in devel...
Since many children with Down syndrome have difficulties with emotion recognition, there is a significant application gap in assistive technologies and affective computing that could be addressed. Conventional deep learning methods, which depend on t...
Facial Emotion Recognition (FER) has transformed human–computer interaction, yet its application to understanding relationships between physical environments and human emotional responses remains an emerging area within urban studies. This stud...
Skeleton-based human action recognition has made great progress, especially with the development of a graph convolution network (GCN). The most important work is ST-GCN, which automatically learns both spatial and temporal patterns from skeleton sequ...
This study examined the influence of genetic background on cognitive performance in a selectively bred high nicotine-preferring (NP) rat line. Using the novel object recognition (NOR), novel location recognition (NLR), and Morris water maze (MWM) tes...
In cross-corpus scenarios, inappropriate feature-processing methods tend to cause the loss of key emotional information. Additionally, deep neural networks contain substantial redundancy, which triggers domain shift issues and impairs the generalizat...
Traditional water hazard monitoring often relies on manual inspection and water level sensors, typically lacking in accuracy and real-time capabilities. However, the method of using video surveillance for monitoring water hazard characteristics can c...
In haptic recognition, pressure information is usually represented as an image, and then used for feature extraction and classification. Deep learning that processes haptic information in end-to-end manner has attracted attention. This study proposes...
12 August 2021
Although creating a high-quality urban green space (UGS) is of considerable importance in public health, few studies have used individuals’ emotions to evaluate the UGS quality. This study aims to conduct a multidimensional emotional assessment metho...
In the skeleton-based human action recognition domain, the spatial-temporal graph convolution networks (ST-GCNs) have made great progress recently. However, they use only one fixed temporal convolution kernel, which is not enough to extract the tempo...
Engagement ability plays a fundamental role in allocating attentional resources and helps us perform daily tasks efficiently. Therefore, it is of great importance to recognize engagement level. Electroencephalography is frequently employed to recogni...
Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain–computer interfaces. In this paper, we propose TSANN-TG (temporal–spatial attention neural network with a task-specific graph),...
In the field of Chinese-named entity recognition, recent research has sparked new interest by combining lexical features with character-based methods. Although this vocabulary enhancement method provides a new perspective, it faces two main challenge...
In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area o...
Facial expression recognition (FER) in the wild is a challenging task due to some uncontrolled factors such as occlusion, illumination, and pose variation. The current methods perform well in controlled conditions. However, there are still two issues...
Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can b...
Early recognition of abnormal gait enables physicians to determine a prompt rehabilitation plan for patients for the most effective treatment and care. The Kinect depth sensor can easily collect skeleton data describing the position of joints in the...
Recently, studies on driver inattention state recognition as an advanced mobility application technology are being actively conducted to prevent traffic accidents caused by driver drowsiness and distraction. The driver inattention state recognition s...
The Graph Convolutional Neural Networks (GCN) method has shown excellent performance in the field of deep learning, and using graphs to represent speech data is a computationally efficient and scalable approach. In order to enhance the adequacy of gr...
Hand gesture recognition plays an important role in computer vision with broad applications in human–computer interaction, intelligent perception, and contactless interaction systems. However, existing detection-based methods mainly rely on han...
Next-generation advanced driver-assistance systems (ADASs) are a promising direction for intelligent transportation systems. To achieve intelligent security monitoring, it is imperative that vehicles possess the ability to accurately comprehend drive...
Traffic sign recognition (TSR) is a noteworthy issue for real-world applications such as systems for autonomous driving as it has the main role in guiding the driver. This paper focuses on Taiwan’s prohibitory sign due to the lack of a database...
In recent years, spatial-temporal graph convolutional networks have played an increasingly important role in skeleton-based human action recognition. However, there are still three major limitations to most ST-GCN-based approaches: (1) They only use...
Human action recognition is a computer vision task that identifies how a person or a group acts on a video sequence. Various methods that rely on deep-learning techniques, such as two- or three-dimensional convolutional neural networks (2D-CNNs, 3D-C...
Depression-induced cognitive impairment has recently been given more attention in research. However, the relationship between depression and different types of memory is still not clear. Chronic unpredictable mild stress (CUMS) is a commonly used ani...
High spatial resolution remote sensing image (HSRRSI) data provide rich texture, geometric structure, and spatial distribution information for surface water bodies. The rich detail information provides better representation of the internal components...
Synthetic aperture radar (SAR) can acquire maritime imagery independently of daylight and under diverse weather conditions, which makes it well suited to persistent ship monitoring and maritime awareness. Nevertheless, recognizing SAR ships from only...
Computer vision recognition technology, due to its non-invasive and convenient nature, can effectively avoid damage to fragile wild plants during recognition. However, balancing model complexity, recognition accuracy, and data processing difficulty o...
Skeleton-based action recognition networks have widely adopted the approach of Graph Convolutional Networks (GCN) due to their superior capabilities in modeling data topology, but several key issues still require further investigation. Firstly, the g...
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