Recently, deep neural networks (DNNs) have emerged as the leading approach for low-light image enhancement (LLIE). However, training these models requires large-scale paired datasets, which are challenging to obtain d...
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Semi-supervised adversarial transfer gaining knowledge of (SATL) has been proposed as a powerful method for automatic pores and skin lesion segmentation. This approach aims to transfer knowledge from a categorized sup...
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This research work aims to develop an image captioning system utilizing deep learning techniques. The pre-trained VGG-16 model is employed to extract image features, while an innovative encoder-decoder architecture is...
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Recent studies have indicated that circular RNAs (circRNAs) play a significant role in the diagnosis and treatment of disease. However, the prediction of associations between circRNAs and diseases using conventional b...
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This research proposes an innovative method for categorizing phishing and legitimate websites based on content features extracted from their HTML structure. Leveraging data collection with the requests library and HTM...
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Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, s...
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Visual sensors are indispensable for automatic vehicles, to achieve comprehensive environmental perception for navigation, but their deteriorated performance in harsh illuminations largely sets back the practical use ...
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Visual sensors are indispensable for automatic vehicles, to achieve comprehensive environmental perception for navigation, but their deteriorated performance in harsh illuminations largely sets back the practical use of autonomous driving technologies. A promising solution is to use a bio-inspired event sensor that asynchronously records the intensity changes with high sensitivity, fast response, and large dynamic range, which assists situational awareness of moving vehicles in harshly lit scenarios. However, the sensing of event sensors comes with heavy noise and sparse signals, due to either severe photon starvation or limited acquisition bandwidth. In this paper, we propose an approach for real-time sketching of the harshly lit driving environment (RIDE), to outline the driving surroundings from noisy sporadic measurements. We address confronted challenges as follows: (i) map the raw event signals into a low dimensional space and cluster the features to depict the spatial-temporal correlation within raw events;(ii) design a general inference network to construct continuous motion fields of the scene from the encoded features of noisy sporadic raw measurements;(iii) construct the pseudo-ground-truth via the unsupervised motion compensation as the label of the above network learning, achieving real-time inference. Our approach is experimentally validated on real traffic data and displays high-fidelity perception capability for extremely dark scenes and scenarios with high dynamic range. Also, we investigate RIDE's effectiveness in the downstream task—detection of traffic participants. In a nutshell, the proposed RIDE provides high-fidelity sensing of harshly lit environments and lays the foundation for the all-day visual navigation of autonomous vehicles. IEEE
The Dynamic Emotion-Adaptive Attention Mechanism (DEAAM) offers a groundbreaking framework for analyzing emotions in real-time video streams, leveraging a state-of-the-art convolution neural network (CNN) for rapid an...
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Deep mastering the usage of convolutional neural Nets (CNNs) has been a popular technique for image categories. A CNN is designed to inversely clear up the mission today's representing input data in a significant ...
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As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial attacks manifest in two primary forms: deductive illusion, where speci...
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