Semantic segmentation in rainy scenes is a challenging task due to the complex environment, class distribution imbalance, and limited annotated data. To address these challenges, we propose a novel framework that util...
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Anomaly detection is recognized as a critical task in machine learning, particularly when applied to high-dimensional and graph structured data. Traditional clustering based methods for anomaly detection are often cha...
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As trivial as it is for humans to identify, locate, and distinguish objects from their backgrounds and other objects, for computersystems, it is not so trivial. In computer Science domain, the topic of Object detecti...
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Photovoltaic systems are being used more and more in the production of electricity because of the high cost of fossil fuel-based energy. The main issue that happens between grid and end-user transmission lines is powe...
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Safe and effective vaccines go a long way in preventing severe disease and limiting the spread of virus. Before human trials, these vaccines are required to be tested on animal subjects. The response of animals to the...
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To provide the industry with an algorithm capable of accurately calculating comment statement tags, this paper proposes a research method based on feature engineering and BILSTM technology. First, the paper uses big d...
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We present methods for conditional and residual coding in the context of scalable coding for humans and machines. Our focus is on optimizing the rate-distortion performance of the reconstruction task using the informa...
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In this paper we present C-HAR, a low-cost,low-complexity, compressive measurement-based human activity recognitionframework for embedded devices, capable of simultaneously filtering andacquiring the sensor readings o...
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While there has been considerable progress in quality assessment for various types of media, evaluating the quality of point clouds remains a major challenge due to the complexity of the associated applications and th...
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