In recent years, remote sensing object detection has become a research hotspot in computer vision tasks. However, previous approaches for remote sensing object detection often overlook the rich contextual information ...
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As deep learning advances, neural network technologies are increasingly penetrating the field of steel surface defect detection. To tackle the challenges of low accuracy and inadequate quality, we introduce CMS-YOLOv8...
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Steel, being a widely utilized material in industrial production, holds a pivotal role in ensuring product safety and longevity. Hence, the exploration and implementation of steel surface defect detection technology c...
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In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users nee...
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Three-dimensional(3D)surface geometry provides elemental information in various sciences and precision *** Projection Profilometry(FPP)is one of the most powerful non-contact(thus non-destructive)and non-interferometr...
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Three-dimensional(3D)surface geometry provides elemental information in various sciences and precision *** Projection Profilometry(FPP)is one of the most powerful non-contact(thus non-destructive)and non-interferometric(thus less restrictive)3D measurement techniques,featuring at its high ***,the measurement precision of FPP is currently evaluated experimentally,lacking a complete theoretical model for *** propose the first complete FPP precision model chain including four stage models(camera intensity,fringe intensity,phase and 3D geometry)and two transfer models(from fringe intensity to phase and from phase to 3D geometry).The most significant contributions include the adoption of a non-Gaussian camera noise model,which,for the first time,establishes the connection between camera’s electronics parameters(known in advance from the camera manufacturer)and the phase precision,and the formulation of the phase to geometry transfer,which makes the precision of the measured geometry representable in an explicit and concise *** a result,we not only establish the full precision model of the 3D geometry to characterize the performance of an FPP system that has already been set up,but also explore the expression of the highest possible precision limit to guide the error distribution of an FPP system that is yet to *** theoretical models make FPP a more designable technique to meet the challenges from various measurement demands concerning different object sizes from macro to micro and requiring different measurement precisions from a few millimeters to a few micrometers.
The underwater environment is complex and diverse, making it challenging to locate aquatic organisms accurately. The precise identification of underwater animals is crucial for ecological research and fisheries manage...
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In recent years, infrared target detection has played a crucial role in intelligent transportation and assisted driving. Addressing the current issues of low detection accuracy, poor robustness, and missed detections ...
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Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion...
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Emotion recognition plays a crucial role in various fields and is a key task in natural language processing (NLP). The objective is to identify and interpret emotional expressions in text. However, traditional emotion recognition approaches often struggle in few-shot cross-domain scenarios due to their limited capacity to generalize semantic features across different domains. Additionally, these methods face challenges in accurately capturing complex emotional states, particularly those that are subtle or implicit. To overcome these limitations, we introduce a novel approach called Dual-Task Contrastive Meta-Learning (DTCML). This method combines meta-learning and contrastive learning to improve emotion recognition. Meta-learning enhances the model’s ability to generalize to new emotional tasks, while instance contrastive learning further refines the model by distinguishing unique features within each category, enabling it to better differentiate complex emotional expressions. Prototype contrastive learning, in turn, helps the model address the semantic complexity of emotions across different domains, enabling the model to learn fine-grained emotions expression. By leveraging dual tasks, DTCML learns from two domains simultaneously, the model is encouraged to learn more diverse and generalizable emotions features, thereby improving its cross-domain adaptability and robustness, and enhancing its generalization ability. We evaluated the performance of DTCML across four cross-domain settings, and the results show that our method outperforms the best baseline by 5.88%, 12.04%, 8.49%, and 8.40% in terms of accuracy.
The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generat...
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This paper proposes a Poor and Rich Squirrel Algorithm (PRSA)-based Deep Maxout network to find fraud data transactions in the credit card system. Initially, input transaction data is passed to the data transformation...
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