In the digital era, the continuous advancement of computertechnology has profoundly impacted the field of clinical medicine. Surgical navigation, as a key application of computer-assisted navigation, has played a piv...
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Introducing persona information in dialogue generation helps to make replies more human-like. However, how to solve the problem of ignoring the persona of the speakers in dialogue generation on persona-spare data, and...
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Satisfiability problem has significant applications in many fields, such as software verification and Robot path planning. Researchers have proposed many reduction rules for the conjunctive normal form formula, some o...
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A self-driving car is a topic deserving of intense attention from both research & development folks and industry experts as self-driving cars have latent potential to revolutionize transportation systems around th...
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ISBN:
(纸本)9798350305258
A self-driving car is a topic deserving of intense attention from both research & development folks and industry experts as self-driving cars have latent potential to revolutionize transportation systems around the whole car-driving world. Safety and reliability of the automobile are the topics most important to people when they talk about self-driving cars, which heavily rely on accurate and robust object detection in a myriad of environmental conditions. So, an approach for improving the capability of self-driving cars for object detection being driven in smoggy conditions is put forth. Detecting objects & vehicles on the road is a core part of driverless car technology as it requires high accuracy and real-time processing to ensure safety in various driving scenarios. This research proposes an approach that has been improved and is related to the YOLO (You Only Look Once) algorithm to understand the presence of vehicles & objects on the road when the weather is foggy. Our approach involves integrating & incorporating one component for dehazing into the YOLO model to improve restoring of image info which we achieved with the help of a technology called MSRCR (Multi-Scale Retinex with Colour Restoration). We have trained the updated scenario using augmented data processed with MSRCR to improve its stability and performance. We conducted extensive evaluations on a publicly available dataset and the results clearly indicate that our enhanced YOLO model outperforms conventional YOLO in detecting vehicles in foggy weather conditions. Our findings highlight the latent possibility of mixing multiple technologies to improve object detection for self-driving cars which could improve vehicle safety and vehicle reliability of autonomous vehicles for users in the future. Our approach can be further extended to other applications that require accurate and robust object detection in extreme conditions such as in robotics, surveillance systems, security systems & satellite image
Cross-modal retrieval often faces the challenges of eliminating modality gap, learning robust modality invariance and semantic discrimination. Existing self-supervised crossmodal approaches still suffer from the fault...
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Worldwide, heart disease is the leading cause of mortality. By providing proper therapy, early identification of heart disease can lower the likelihood of the illness advancing to a more severe level. It is possible t...
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Deep learning technology is now applied in many fields. In the financial field, deep learning as an emerging technology is also widely used, such as high-frequency trading, investment portfolio, stock price prediction...
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This This article introduces a novel approach to image compression through the utilization of autoencoders, a class of neural networks adept at learning to distill an image's essential attributes and compactly rep...
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Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of ***,few methods exist for generating full-body animations,and the po...
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Background Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of ***,few methods exist for generating full-body animations,and the portability of virtual character gestures and facial animations has not received sufficient *** Therefore,we propose a deep-learning-based audio-to-animation-and-blendshape(Audio2AB)network that generates gesture animations and ARK it's 52 facial expression parameter blendshape weights based on audio,audio-corresponding text,emotion labels,and semantic relevance labels to generate parametric data for full-body *** parameterization method can be used to drive full-body animations of virtual characters and improve their *** the experiment,we first downsampled the gesture and facial data to achieve the same temporal resolution for the input,output,and facial *** Audio2AB network then encoded the audio,audio-corresponding text,emotion labels,and semantic relevance labels,and then fused the text,emotion labels,and semantic relevance labels into the audio to obtain better audio ***,we established links between the body,gestures,and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss *** By using audio,audio-corresponding text,and emotional and semantic relevance labels as input,the trained Audio2AB network could generate gesture animation data containing blendshape ***,different 3D virtual character animations could be created through *** The experimental results showed that the proposed method could generate significant gestures and facial animations.
Traction systems provide the traction power of high-speed trains. Because the complex operation mechanism of train under actual working conditions and the measured data are nonlinear and non-Gaussian, and the sampling...
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