Third-party library (TPL) reuse may introduce vulnerable or malicious code and expose the software, which exposes them to potential risks. Thus, it is essential to identify third-party dependencies and take immediate corrective action to fix critical...
Binary code similarity detection (BCSD) plays a vital role in computer security and software engineering. Traditional BCSD methods heavily rely on specific features and necessitate rich expert knowledge, which are sensitive to code alterations. To im...
The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combin...
SLAM is regarded as a fundamental task in mobile robots and AR, implementing localization and mapping in certain circumstances. However, with only RGB images as input, monocular SLAM systems suffer problems of scale ambiguity and tracking difficulty...
With the rapid development of remote sensing (RS) observation technology over recent years, the high-level semantic association-based cross-modal retrieval of RS images has drawn some attention. However, few existing studies on cross-modal retrieval...
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...
This paper strives to improve the performance of video–text retrieval. To date, many algorithms have been proposed to facilitate the similarity measure of video–text retrieval from the single global semantic to multi-level semantics. Howe...
Real-time semantic segmentation is a challenging task in computer vision, especially in complex scenes. In this study, a novel three-branch semantic segmentation model is designed, aiming to effectively use boundary information to improve the accurac...
Building function identification plays a crucial role in providing basic data for urban planning, management, and various intelligent applications. Today, building function identification methods using Street View Images (SVIs) have made significant...
The semantic social network is a complex system composed of nodes, links, and documents. Traditional semantic social network community detection algorithms only analyze network data from a single view, and there is no effective representation of sema...
Discriminative feature learning is the key to remote sensing scene classification. Previous research has found that most of the existing convolutional neural networks (CNN) focus on the global semantic features and ignore shallower features (low-leve...
The multi-scale characteristics of remote sensing imagery have an inherent correspondence with the hierarchical structure of land cover classification systems, providing a theoretical foundation for multi-level land cover classification. However, mos...
Semantic change detection technology based on remote sensing data holds significant importance for urban and rural planning decisions and the monitoring of ground objects. However, simple convolutional networks are limited by the receptive field, can...
We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consist...
Semantic segmentation is crucial for a wide range of downstream applications in remote sensing, aiming to classify pixels in remote sensing images (RSIs) at the semantic level. The dramatic variations in grayscale and the stacking of categories withi...
Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitorin...
Semantic segmentation of three-dimensional (3D) plant point clouds at the stem-leaf level is foundational and indispensable for high-throughput tomato phenotyping systems. However, existing semantic segmentation methods often suffer from issues such...
Vehicle re-identification across multiple cameras is one of the main problems of intelligent transportation systems (ITSs). Since the differences in the appearance between different vehicles of the same model are small and the appearance of the same...
Existing indoor semantic recognition schemes are mostly capable of discovering patterns through smartphone sensing, but it is hard to recognize rich enough high-level indoor semantics for map enhancement. In this work we present DeepMap+, an automati...
In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attent...
The need to enrich the semantic completeness of OpenStreetMap (OSM) data is crucial for its effective use in geographic information systems and urban studies. Addressing this challenge, our research introduces a novel hierarchical feature augmentatio...
To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically...
Aspect-level sentiment classification aims to predict the sentiment polarities towards the target aspects given in sentences. To address the issues of insufficient semantic information extraction and high computational complexity of attention mechani...
Most educators agree that essays are the best way to evaluate students’ understanding, guide their studies, and track their growth as learners. Manually grading student essays is a tedious but necessary part of the learning process. Automated E...
Recently, searchable encrypted image retrieval in a cloud environment has been widely studied. However, the inappropriate encryption mechanism and single feature description make it hard to achieve the expected effects. Therefore, a major challenge o...
Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually l...
The mainstream methods for change detection in synthetic-aperture radar (SAR) images use difference images to define the initial change regions. However, methods can suffer from semantic collapse, which makes it difficult to determine semantic inform...
Recent research has shown the advantages of incorporating multisource geospatial data into the classification of urban functional zones (UFZs), particularly remote sensing and social sensing data. However, the effects of combining datasets of varying...
With the rapid development of cities, semantic segmentation of urban scenes, as an important and effective imaging method, can accurately obtain the distribution information of typical urban ground features, reflecting the development scale and the l...
While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth...
With the rapid development of industry, people’s requirements for the functionality, stability, and safety of electronic products are becoming higher and higher. As an important medium for power supply and information transmission functions of...
For the actual collected point cloud data, there are widespread challenges such as semantic inconsistency, density variations, and sparse spatial distribution. A network called PointMM is developed in this study to enhance the accuracy of point cloud...
Simultaneous Localization and Mapping (SLAM) is a fundamental technique in mobile robotics, enabling autonomous navigation and environmental reconstruction. However, dynamic elements in real-world scenes—such as walking pedestrians, moving vehi...
There is a close correlation between brain aging and age. However, traditional neural networks cannot fully capture the potential correlation between age and brain aging due to the limited receptive field. Furthermore, they are more concerned with de...
Emotion matching prediction between music and video segments is essential for intelligent mobile sensing systems, where multimodal affective cues collected from smart devices must be jointly analyzed for context-aware media understanding. However, tr...
The purpose of few-shot relation extraction (RE) is to recognize the relationship between specific entity pairs in text when there are a limited number of labeled samples. A few-shot RE method based on a prototype network, which constructs relation p...
Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill de...
Aerial images record the dynamic Earth terrain, reflecting changes in land cover patterns caused by natural processes and human activities. Nonetheless, prevailing aerial image classification methodologies predominantly function within a closed-set f...
A parallel high-resolution underwater target detection network is proposed to address the problems of complex underwater scenes and limited target feature extraction capability. First, a high-resolution network (HRNet), a lighter high-resolution huma...
Language-based person search retrieves images of a target person using natural language description and is a challenging fine-grained cross-modal retrieval task. A novel hybrid attention network is proposed for the task. The network includes the foll...
Deep learning methods have gained significant popularity in the field of polarimetric synthetic aperture radar (PolSAR) image classification. These methods aim to extract high-level semantic features from the original PolSAR data to learn the polarim...
In recent years, the development of deep learning has brought great convenience to the work of target detection, semantic segmentation, and object recognition. In the field of infrared weak small target detection (e.g., surveillance and reconnaissanc...
As a core task of computer vision perception, 3D scene understanding has received widespread attention. However, the current research mainly focuses on the semantic understanding task at the level of entity objects and often neglects the semantic rel...
In disaster emergency response, spatial location information embedded within social media texts holds substantial value for the rapid localization of affected areas and the implementation of precise rescue operations. Existing research predominantly...
Semantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder str...
The semantic segmentation of annual rings is a research topic of interest in wood chronology. To solve the problem of wood annual rings being difficult to segment in dense areas and being greatly affected by defects such as cracks and wormholes, this...
In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semanti...
Drug-drug interactions (DDIs) may bring huge health risks and dangerous effects to a patient’s body when taking two or more drugs at the same time or within a certain period of time. Therefore, the automatic extraction of unknown DDIs has great...
In contrast to previous descriptions of on the contrary (OTC) as a corrective or replacive marker requiring explicit negation, this study revealed that more than 50% of the occasions the expression was used in British tertiary level student essays, r...
The continuous accumulation of multi-dimensional data and the development of Semantic Web and Linked Data published in the Resource Description Framework (RDF) bring new requirements for data analytics tools. Such tools should take into account the s...
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