Semantic segmentation of remote sensing imagery is a fundamental task in intelligent interpretation. Since deep convolutional neural networks (DCNNs) performed considerable insight in learning implicit representations from data, numerous works in rec...
In this paper, we propose a novel method to remove haze from a single hazy input image based on the sparse representation. In our method, the sparse representation is proposed to be used as a contextual regularization tool, which can reduce the block...
Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of dr...
Transmission line inspection plays an important role in maintaining power security. In the object detection of the transmission line, the large-scale gap of the fittings is still a main and negative factor in affecting the detection accuracy. In this...
Sentiment classification plays a crucial role in evaluating user feedback. Today, online media users can freely provide their reviews with few restrictions. User reviews on social media are often disorganized and challenging to classify as positive o...
Considering the ever-growing volume of electronic documents made available in our daily lives, the need for an efficient tool to capture their gist increases as well. Automatic text summarization, which is a process of shortening long text and extrac...
Contextual representation has taken center stage in Natural Language Processing (NLP) in the recent past. Models such as Bidirectional Encoder Representations from Transformers (BERT) have found tremendous success in the arena. As a first attempt in...
In recent years, graph-based learning methods have gained significant traction in point-of-interest (POI) recommendation systems due to their strong generalization capabilities. These approaches commonly transform user check-in records into graph-str...
Current two-stage object detectors extract the local visual features of Regions of Interest (RoIs) for object recognition and bounding-box regression. However, only using local visual features will lose global contextual dependencies, which are helpf...
Change detection based on bi-temporal remote sensing images has made significant progress in recent years, aiming to identify the changed and unchanged pixels between a registered pair of images. However, most learning-based change detection methods...
Remote sensing object detection is a critical task in Earth observation. Despite the remarkable progress made in general object detection, existing detectors struggle with remote sensing scenarios due to the prevalence of numerous small objects with...
Users’ activities in location-based social networks (LBSNs) can be naturally transformed into graph structural data, and more advanced graph representation learning techniques can be adopted for analyzing user preferences, which benefits a vari...
Two universal functional principles of Grossberg’s Adaptive Resonance Theory decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental contex...
Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yield excell...
Oriented object detection is a fundamental and challenging task in remote sensing image analysis that has recently drawn much attention. Currently, mainstream oriented object detectors are based on densely placed predefined anchors. However, the high...
Named entity recognition (NER) is a fundamental task in natural language processing. In Chinese NER, additional resources such as lexicons, syntactic features and knowledge graphs are usually introduced to improve the recognition performance of the m...
As social media platforms offer a medium for opinion expression, social phenomena such as hatred, offensive language, racism, and all forms of verbal violence have increased spectacularly. These behaviors do not affect specific countries, groups, or...
Automatic medical image segmentation is an essential step toward accurate diseases diagnosis and designing a follow-up treatment. This assistive method facilitates the cancer detection process and provides a benchmark to highlight the affected area....
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...
In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR) image classification is investigated using sparse repres...
Synthetic-aperture radar (SAR) image target detection is widely used in military, civilian and other fields. However, existing detection methods have low accuracy due to the limitations presented by the strong scattering of SAR image targets, unclear...
Visual relationship detection (VRD), a challenging task in the image understanding, suffers from vague connection between relationship patterns and visual appearance. This issue is caused by the high diversity of relationship-independent visual appea...
We argue that the usual Bloch sphere is insufficient in various aspects for the representation of qubits in quantum information theory. For example, spin flip operations with the quaternions
As social media platforms continue their exponential growth, so do the threats targeting their security. Detecting disguised spam messages poses an immense challenge owing to the constant evolution of tactics. This research investigates advanced arti...
12 May 2022
The increasing expansion of biomedical documents has increased the number of natural language textual resources related to the current applications. Meanwhile, there has been a great interest in extracting useful information from meaningful coherent...
The detection of fake news has emerged as a crucial area of research due to its potential impact on society. In this study, we propose a robust methodology for identifying fake news by leveraging diverse aspects of language representation and incorpo...
Over the past decade, the field of object detection has advanced remarkably, especially in the accurate recognition of medium- and large-sized objects. Nevertheless, detecting small objects is still difficult because their low-resolution appearance p...
The term concept has been a prominent part of investigations in psychology and neurobiology where, mostly, it is mathematically or theoretically represented. Concepts are also studied in the computational domain through their symbolic, distributed an...
To semantically understand remote sensing images, it is not only necessary to detect the objects in them but also to recognize the semantic relationships between the instances. Scene graph generation aims to represent the image as a semantic structur...
The dairy industry has a long supply chain that involves dairy farmers, enterprises, consumers, and the government. The stable growth of consumer groups is the driving force for the sustainable development of the dairy industry. However, in recent ye...
The quantity and quality of cropland are the key to ensuring the sustainable development of national agriculture. Remote sensing technology can accurately and timely detect the surface information, and objectively reflect the state and changes of the...
This study presents the theoretical depth of urban research by proposing a four-stage contextual conceptual guide for integrating historical and societal contextual factors within the nexus of time and space. Addressing a critical gap in urban resear...
Relation extraction tasks aim to predict potential relations between entities in a target sentence. As entity mentions have ambiguity in sentences, some important contextual information can guide the semantic representation of entity mentions to impr...
As an artificial space extended from the physical environment, the virtual environment (VE) provides more possibilities for humans to work and be entertained with less physical restrictions. Benefiting from anonymity, one of the important features of...
In this paper, we focus on the problem of contextual aggregation in the semantic segmentation of aerial images. Current contextual aggregation methods only aggregate contextual information within specific regions to improve feature representation, wh...
The accuracy of traditional topic models may be compromised due to the sparsity of co-occurring vocabulary in the corpus, whereas conventional word embedding models tend to excessively prioritize contextual semantic information and inadequately captu...
In the process of semantic capture, traditional sentence representation methods tend to lose a lot of global and contextual semantics and ignore the internal structure information of words in sentences. To address these limitations, we propose a sent...
Text vectorization is the basic work of natural language processing tasks. High-quality vector representation with rich feature information can guarantee the quality of entity recognition and other downstream tasks in the field of traditional Chinese...
Spatiotemporal prediction is challenging due to extracting representations being inefficient and the lack of rich contextual dependences. A novel approach is proposed for spatiotemporal prediction using a dual memory LSTM with dual attention neural n...
Perceiving discrimination in workplace practices psychologically damages employees and affects their work performance. The current study aims to find differences in perceived diversity practices (i.e., equal representation and developmental opportuni...
This paper proposes a novel technique for representing templates and instances of concept classes. A template representation refers to the generic representation that captures the characteristics of an entire class. The proposed technique uses end-to...
Fine-grained representation is fundamental to species classification based on deep learning, and in this context, cross-modal contrastive learning is an effective method. The diversity of species coupled with the inherent contextual ambiguity of natu...
Accurate target recognition of unmanned aerial vehicles (UAVs) in the intelligent warfare mode relies on a highly standardized UAV knowledge base, and thus it is crucial to construct a knowledge graph suitable for UAV multi-source information fusion....
The growing complexity and interdependence of healthcare data, especially for chronic diseases such as asthma, demand innovative approaches for effective knowledge representation. This study introduces a general contextual ontology model for chronic...
Considering the complexity of entity pair relations and the information contained in the target neighborhood in few-shot knowledge graphs (KG), existing few-shot KG completion methods generally suffer from insufficient relation representation learnin...
Sarcasm is a sophisticated figurative language that is prevalent on social media platforms. Automatic sarcasm detection is significant for understanding the real sentiment tendencies of users. Traditional approaches mostly focus on content features b...
Social representations theory provides a key lens through which to approach mixed racial and ethnic identities. The concept and contextual histories of “mixedness” highlight how meanings are ascribed and constructed, and social representations of mix...
Quantum contextuality plays a significant role in supporting quantum computation and quantum information theory. The key tools for this are the Kochen–Specker and non-Kochen–Specker contextual sets. Traditionally, their representation has...
Considerable research has been conducted to obtain translations that reflect contextual information in documents and simultaneous interpretations. Most of the existing studies use concatenation data which merge previous and current sentences for trai...
Background: Understanding visual context in images is essential for enhanced Point-of-Interest (POI) recommender systems. Traditional models often rely on global features, overlooking object-level information, which can limit contextual accuracy. Met...
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