卷发
发表于 2025-3-23 09:57:29
Deep Neural Network Acceleration Method Based on Sparsityetwork (DNN) based on high-performance GPU and CPU devices has achieved remarkable results in the fields of object detection and recognition. The DNNs have also been applied to social media, image processing and video processing. With the improvement of neural networks, the depth and complexity of v
启发
发表于 2025-3-23 16:36:45
Feature-Selecting Based Hashing via Deep Convolutional Neural Networksl representatives. In recent years, with the development of deep convolutional neural networks, there have been many deep hashing algorithms for image retrieval. This paper proposes a new deep hashing algorithm that adds a hash layer to the image classification networks to obtain hash codes. A const
induct
发表于 2025-3-23 21:58:05
Efficient and Robust Homography Estimation Using Compressed Convolutional Neural Networkd robust homography estimation algorithm is extremely necessary. In this paper, we design an innovative compressed convolutional neural network to estimate homographies which work very well. The model size of the network is less than 10 MB, which is small enough to be used on mobile devices. In addi
完整
发表于 2025-3-23 22:50:31
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别名
发表于 2025-3-24 02:59:28
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SPALL
发表于 2025-3-24 07:03:48
Real-Time Semantic Mapping of Visual SLAM Based on DCNNy visual SLAM system only contain low-level information. The unmanned system can work better if high-level semantic information is included. In this paper, we proposed a visual semantic SLAM method using DCNN (Deep Convolution Neural Network). The network is composed of feature extraction, multi-sca
Certainty
发表于 2025-3-24 13:27:46
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注意到
发表于 2025-3-24 17:45:14
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Deference
发表于 2025-3-24 20:11:33
1865-0929 organized in topical sections on image processing; machine learning; quality assessment; telecommunications; video coding; video surveillance; virtual reality..978-981-13-8137-9978-981-13-8138-6Series ISSN 1865-0929 Series E-ISSN 1865-0937
Silent-Ischemia
发表于 2025-3-25 00:54:26
Egon Bahr – zur besonderen Verwendungraint item be added to the classification loss function, which is used to pick out some important nodes from the hash layer, and these selected nodes representing the picture are encoded. Compared with other existing algorithms, the performance of our algorithm has a certain improvement.