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...
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...
Recently, hyperspectral image (HSI) classification has attracted increasing attention in the remote sensing field. Plenty of CNN-based methods with diverse attention mechanisms (AMs) have been proposed for HSI classification due to AMs being able to...
Tea disease detection is crucial for improving the agricultural circular economy. Deep learning-based methods have been widely applied to this task, and the main idea of these methods is to extract multiscale coarse features of diseases using the bac...
Hyperspectral imagery (HSI) with high spectral resolution contributes to better material discrimination, while the spatial resolution limited by the sensor technique prevents it from accurately distinguishing and analyzing targets. Though generative...
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...
The traditional Siamese object tracking algorithm uses a convolutional neural network as the backbone and has achieved good results in improving tracking precision. However, due to the lack of global information and the use of spatial and scale infor...
Hyperspectral image (HSI) classification aims to recognize categories of objects based on spectral–spatial features and has been used in a wide range of real-world application areas. Attention mechanisms are widely used in HSI classification fo...
There is a debate about whether working memory (WM) representations are individual features or bound objects. While spatial attention is reported to play a significant role in feature binding, little is known about the role of spatial attention in WM...
Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researc...
Convolutional neural-network-based autoencoders, which can integrate the spatial correlation between pixels well, have been broadly used for hyperspectral unmixing and obtained excellent performance. Nevertheless, these methods are hindered in their...
Visual perception is profoundly modulated by spatial attention, which can selectively prioritize goal-related information. Previous studies found spatial attention facilitated the efficacy of neuronal communication between visual cortices with hierar...
Single image dehazing is a highly challenging ill-posed problem. Existing methods including both prior-based and learning-based heavily rely on the conceptual simplified atmospheric scattering model by estimating the so-called medium transmission map...
Existing research has found that spatial attention alters how various stimulus properties are perceived (e.g., luminance, saturation), but few have explored whether it improves the accuracy of perception. To address this question, we performed two ex...
The Spatial-Numerical Association of Response Codes (SNARC) suggests the existence of an association between number magnitude and response position, with faster left-key responses to small numbers and faster right-key responses to large numbers. The...
We investigated exogenous and endogenous orienting of visual attention to the spatial location of an auditory cue. In Experiment 1, significantly faster saccades were observed to visual targets appearing ipsilateral, compared to contralateral, to the...
The spatial behavior of tourists is an important part of the research on congestion management and sustainable planning of tourism destinations. Combined with user-generated content (UGC) and site-based survey data, this study conducted an overlaying...
Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a ch...
Underwater target detection is the foundation and guarantee for the autonomous operation of underwater vehicles and is one of the key technologies in marine exploration. Due to the complex and special underwater environment, the detection effect is p...
25 February 2023
This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes...
Recently, deep learning-based classification approaches have made great progress and now dominate a wide range of applications, thanks to their Herculean discriminative feature learning ability. Despite their success, for hyperspectral data analysis,...
Most people are good at estimating summary statistics for different features of groups of objects. For instance, people can selectively attend to different features of a group of lines and report ensemble properties such as the mean length or mean or...
Link prediction for opportunistic networks faces the challenges of frequent changes in topology and complex and variable spatial-temporal information. Most existing studies focus on temporal or spatial features, ignoring ample potential information....
Accurate traffic flow forecasting is pivotal for intelligent traffic control and guidance. Manually capturing the intricate dependencies between spatial and temporal dimensions in traffic data presents a significant challenge. Prior methods have prim...
Spatial resolution enhancement in remote sensing data aims to augment the level of detail and accuracy in images captured by satellite sensors. We proposed a novel spatial resolution enhancement framework using the convolutional attention-based token...
Precise object counting is crucial in practical applications, finding extensive utility across numerous societal domains. In the context of few-shot object counting, variations in object angles can significantly alter the distribution and distinguish...
Recently, many deep learning-based methods have been successfully applied to hyperspectral image (HSI) classification. Nevertheless, training a satisfactory network usually needs enough labeled samples. This is unfeasible in practical applications si...
Facial Expression Recognition (FER) is a research topic of great practical significance. However, existing FER methods still face numerous challenges, particularly in the interaction between spatial and global information, the distinction of subtle e...
Previous research on the relationship between attention and emotion processing have focused essentially on consciously-viewed, supraliminal stimuli, while the attention-emotion interplay remains unexplored in situations where visual awareness is rest...
In order to categorize feature classes by capturing subtle differences, hyperspectral images (HSIs) have been extensively used due to the rich spectral-spatial information. The 3D convolution-based neural networks (3DCNNs) have been widely used in HS...
Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data. Existing graph neural n...
According to the statistics of ship traffic accidents on inland waterways, potential safety hazards such as stranding, hitting rocks, and suspending navigation are on the increase because of the sudden rise and fall of the water level, which may resu...
Band selection (BS) aims to reduce redundancy in hyperspectral imagery (HSI). Existing BS approaches typically model HSI only in a single dimension, either spectral or spatial, without exploring the interactions between different dimensions. To this...
Accurate and reliable prediction of air pollutant concentrations is important for rational avoidance of air pollution events and government policy responses. However, due to the mobility and dynamics of pollution sources, meteorological conditions, a...
Most traditional hyperspectral image (HSI) classification methods relied on hand-crafted or shallow-based descriptors, which limits their applicability and performance. Recently, deep learning has gradually become the mainstream method of HSI classif...
We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimat...
There is limited research on current traffic classification methods for dark web traffic and the classification results are not very satisfactory. To improve the prediction accuracy and classification precision of dark web traffic, a classification m...
The accurate identification of pulmonary nodules is critical for the early diagnosis of lung diseases; however, this task remains challenging due to inadequate feature representation and limited localization sensitivity. Current methodologies often u...
Spaceborne infrared ship detection holds critical strategic significance in both military and civilian domains. As a crucial data source for ship detection, infrared remote sensing imagery offers the advantages of all-weather detection and strong ant...
Aiming to solve the problems of different spectral bands and spatial pixels contributing differently to hyperspectral image (HSI) classification, and sparse connectivity restricting the convolutional neural network to a globally dependent capture, we...
Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this...
Segmenting small infrared targets presents a significant challenge for traditional image processing architectures due to the inherent lack of texture, minimal shape information, and their sparse pixel representation within images. The conventional UN...
Given the problem of spatial detail loss and spectral feature degradation in hyperspectral images (HSIs) characterized as blur, often caused by noise during image acquisition, and methods of removing blur noise designed on HSIs being insufficient, we...
Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images. Given two co-registered images taken at different times, the illumination variations and misregistration errors overwhelm the real o...
Meteorological satellites play a critical role in weather forecasting, climate monitoring, water resource management, and more. These satellites feature an array of radiative imaging bands, capturing dozens of spectral images that span from visible t...
Multi-object tracking (MOT) plays a crucial role in various platforms. Occlusion and insertion among targets, complex backgrounds and higher real-time requirements increase the difficulty of MOT problems. Most state-of-the-art MOT approaches adopt th...
Prostate cancer (PCa) is one of the most prevalent cancers worldwide. As the demand for prostate biopsies increases, a worldwide shortage and an uneven geographical distribution of proficient pathologists place a strain on the efficacy of pathologica...
Music emotion recognition is becoming an important research direction due to its great significance for music information retrieval, music recommendation, and so on. In the task of music emotion recognition, the key to achieving accurate emotion reco...
Video summarization (VS) is a widely used technique for facilitating the effective reading, fast comprehension, and effective retrieval of video content. Certain properties of the new video data, such as a lack of prominent emphasis and a fuzzy theme...
Accurate day-ahead photovoltaic (PV) power forecasting is essential for secure operation and scheduling in power systems with high PV penetration, yet its performance is often constrained by the coarse spatial resolution of operational numerical weat...
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