This paper proposes an encoding–decoding network with a pyramidal representation module, which will be referred to as EDPNet, and is designed for efficient semantic image segmentation. On the one hand, during the encoding process of the propose...
Remote sensing image semantic segmentation plays a crucial role in various fields, such as environmental monitoring, urban planning, and agricultural land classification. However, most current research primarily focuses on utilizing the spatial and s...
Increasing turbulence in contemporary business environments has made the quantitative analysis of unstructured textual data a central methodological challenge for researchers and decision-makers. The increasing availability of large-scale textual dat...
In recent years, large-scale point cloud semantic segmentation has been widely applied in various fields, such as remote sensing and autonomous driving. Most existing point cloud networks use local aggregation to abstract unordered point clouds layer...
Misinformation propagates more rapidly than factual content on social media, presenting significant challenges for automated misinformation detection. Existing approaches often focus solely on textual features without incorporating temporal informati...
In this study, a cross-hierarchical intelligent modeling framework integrating an ecological semantic encoder, a distribution-aligned contrastive loss, and a disturbance-aware attention mechanism was developed to address the semantic alignment challe...
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...
High-resolution remote sensing images often suffer from inadequate fusion between global and local features, leading to the loss of long-range dependencies and blurred spatial details, while also exhibiting limited adaptability to multi-scale object...
Urban unmanned aerial vehicle (UAV) scenes exhibit strong directional structure, and their foreground composition varies substantially across frames. Coordinate Attention encodes directional information through axis-wise pooling, but its gates are pr...
Numerous methods have been proposed for semantic segmentation and the state-of-the-art part is likely to be incorporated by deep learning-based methods which show a salient performance. This study addresses the challenge of semantic segmentation in l...
Remote sensing has now been widely used in various fields, and the research on the automatic land-cover segmentation methods of remote sensing imagery is significant to the development of remote sensing technology. Deep learning methods, which are de...
RGB-T semantic segmentation improves scene understanding under adverse illumination by exploiting complementary visible and thermal cues. However, conventional intermediate fusion networks preserve two modality-specific encoder branches and repeatedl...
Extant Chinese studies have documented that transposing characters within two-character words (e.g., 西装 suit) yields greater parafoveal preview benefits for target words compared to replacing the characters with unrelated ones (e.g., &#...
The fast growth of data in the academic field has contributed to making recommendation systems for scientific papers more popular. Content-based filtering (CBF), a pivotal technique in recommender systems (RS), holds particular significance in the re...
Infrared (IR) and visible image fusion aims to synthesize a composite representation that integrates the thermal target saliency of IR imagery with the textural richness of visible imagery. Existing deep learning-based methods have achieved promising...
Normal aging is commonly accompanied by a decline in cognitive abilities, including memory, yet some individuals maintain these abilities as they get older. We hypothesize that semantic clustering, as an effective strategy for improving performance o...
Contextual information plays a pivotal role in the semantic segmentation of remote sensing imagery (RSI) due to the imbalanced distributions and ubiquitous intra-class variants. The emergence of the transformer intrigues the revolution of vision task...
Previous research has shown a clear self-reference effect in our memory. However, the question arises as to whether this effect could extend to higher cognitive domains such as metamemory. Thus, this study examined the effects of different encoding t...
Semantic segmentation of workpieces and die cavities is critical for intelligent process monitoring and quality control in hammer die-forging. However, the field of 3D point cloud segmentation currently faces prominent limitations in forging scenario...
Geospatial vector data with semantic annotations are a promising but complex data source for spatial prediction tasks such as land use and land cover (LULC) classification. These data describe the geometries and the types (i.e., semantics) of geo-obj...
As a crucial computer vision task, multi-objective semantic segmentation has attracted widespread attention and research in the field of remote sensing image analysis. This technology has important application value in fields such as land resource su...
To address current issues in natural language spatiotemporal queries, including insufficient question semantic understanding, incomplete semantic information extraction, and inaccurate intent recognition, this paper proposes NL2Cypher, a DeBERTa (Dec...
Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly ach...
Semantic communication has emerged as a promising paradigm for next-generation wireless networks, offering substantial efficiency gains by prioritizing the transmission of task-relevant meaning over bit-level accuracy. However, while its benefits in...
With the development of artificial intelligence and deep learning technologies, image captioning has become an important research direction at the intersection of computer vision and natural language processing. The purpose of image captioning is to...
Traditional hyperspectral image semantic segmentation algorithms can not fully utilize the spatial information or realize efficient segmentation with less sample data. In order to solve the above problems, a U-shaped hyperspectral semantic segmentati...
Currently, interest in deep learning-based semantic segmentation is increasing in various fields such as the medical field, automatic operation, and object division. For example, UNet, a deep learning network with an encoder–decoder structure,...
Although ageing is known to affect memory, the precise nature of its effect on retrieval and encoding processes is not well understood. Here, we examine the effect of ageing on the free recall of word lists, in which the semantic structure of word se...
In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for the statistical work of a building area. Recent...
This paper investigates the feasibility of using Large Language Models (LLMs) for the structural analysis of flow-based network data. This analysis is carried out in the presence of a structural difference between the multidimensional numerical space...
Global river monitoring is an important mission within the remote sensing society. One of the main challenges faced by this mission is generating an accurate water mask from remote sensing images (RSI) of rivers (RSIR), especially on a global scale w...
Clouds play a vital role in Earth’s water cycle and the energy balance of the climate system; understanding them and their composition is crucial in comprehending the Earth–atmosphere system. The dataset “Understanding Clouds from S...
Despite the remarkable achievements of modern communication systems based on Shannon’s theory, there is still considerable room for exploration in information transmission capacity, and semantic communication technology has emerged as a promisi...
To address the issue in encoder–decoder-based video captioning method where semantic topics are overlooked in guiding the video captioning, leading to a semantic mismatch between video content and generated captions, a video captioning method b...
Image semantic segmentation has been applied more and more widely in the fields of satellite remote sensing, medical treatment, intelligent transportation, and virtual reality. However, in the medical field, the study of cerebral vessel and cranial n...
Automatic extraction of cerebral vessels and cranial nerves has important clinical value in the treatment of trigeminal neuralgia (TGN) and hemifacial spasm (HFS). However, because of the great similarity between different cerebral vessels and betwee...
The classification and recognition of features play a vital role in production and daily life; however, the current semantic segmentation of remote sensing images is hampered by background interference and other factors, leading to issues such as fuz...
Large language models (LLMs) such as GPT-4 are increasingly integrated into research, industry, and enterprise workflows, yet little is known about how input file formats shape their outputs. While prior work has shown that formats can influence resp...
This research presents the idea of a novel fully-Convolutional Neural Network (CNN)-based model for probabilistic pixel-wise segmentation, titled Encoder-decoder-based CNN for Road-Scene Understanding (ECRU). Lately, scene understanding has become an...
Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of...
Semantic segmentation plays a critical role in understanding complex urban environments, particularly for autonomous driving applications. However, existing approaches face significant challenges under low-light and adverse weather conditions. To add...
Modeling accident investigation reports is crucial for elucidating accident causation mechanisms, analyzing risk evolution processes, and formulating effective accident prevention strategies. However, such reports are typically long, hierarchically s...
With the rapid development of internet and AI technologies, Agricultural Expert Systems (AESs) have become crucial for delivering technical support and decision-making in agricultural management. However, traditional natural language processing metho...
Image semantic segmentation technology has been increasingly applied in many fields, for example, autonomous driving, indoor navigation, virtual reality and augmented reality. However, underwater scenes, where there is a huge amount of marine biologi...
Semantic segmentation provides accurate scene understanding and decision support for many applications. However, many models strive for high accuracy by adopting complex structures, decreasing the inference speed, and making it challenging to meet re...
Autonomous systems are expanding rapidly, driving a demand for robust perception technologies capable of navigating challenging, unstructured environments. While urban autonomy has made significant progress, off-road environments pose unique challeng...
Forest fires are one of the most serious natural disasters that threaten forest resources. The early and accurate identification of forest fires is crucial for reducing losses. Compared with satellites and sensors, unmanned aerial vehicles (UAVs) are...
In this study, we propose a dual-stream UNet neural network architecture design named CPF-UNet, specifically designed for efficient semantic pixel-level segmentation tasks. This architecture cleverly extends the basic structure of the original UNet,...
With the acceleration of digital transformation, open-source software has become a fundamental component of modern smart grids and other critical infrastructures. However, the complex dependency structures of open-source ecosystems and the continuous...
There is a growing interest in scene text detection for arbitrary shapes. The effectiveness of text detection has also evolved from horizontal text detection to the ability to perform text detection in multiple directions and arbitrary shapes. Howeve...
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