In the realm of imageprocessing, the ability to describe an image accurately and comprehensively is paramount. images, whether captured by cameras, generated by computers, or obtained through various means, contain a...
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Brain cancer is one of the most deadly illnesses. It causes abnormal cells to grow in the brain. Planning for treatment and the prognosis of patients with brain tumors depend greatly on early diagnosis. Brain tumors c...
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Plant diseases recognition large crop losses and have negative economic effects, which makes them a serious danger to the world's food security. Early and accurate disease diagnosis is essential for efficient dise...
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image recognition and classification algorithms are more and more widely used in various complex scenes. However, the problems of diverse visual features, occlusion phenomenon, illumination variation and background in...
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This study explores the application of fuzzy logic on the Raspberry Pi platform for classifying Green Series Fischer' s Lovebirds, specifically Green, Jade, Misty, and Olive. African Fischer's lovebirds, known...
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This study explores the implementation of Convolutional neuralnetworks (CNNs) using multiple Graphics processing Units (GPUs) for image recognition tasks, specifically focusing on classifying vegetables and fruits. T...
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image registration is a foundational technique in medical image analysis;however, it is often hindered by issues such as slow processing speed and limited accuracy. In this work, we propose the Visual Mamba Attention ...
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image denoising is one of the fundamental task in digital imageprocessing, aimed at enhancing the visual quality of images by removing unwanted noise. image quality can be compromised by different noise sources, incl...
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Although image super-resolution (SR) problem has experienced unprecedented restoration accuracy with deep neuralnetworks, it has yet limited versatile applications due to the substantial computational costs. Since di...
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ISBN:
(纸本)9798350353013;9798350353006
Although image super-resolution (SR) problem has experienced unprecedented restoration accuracy with deep neuralnetworks, it has yet limited versatile applications due to the substantial computational costs. Since different input images for SR face different restoration difficulties, adapting computational costs based on the input image, referred to as adaptive inference, has emerged as a promising solution to compress SR networks. Specifically, adapting the quantization bit-widths has successfully reduced the inference and memory cost without sacrificing the accuracy. However, despite the benefits of the resultant adaptive network, existing works rely on time-intensive quantization-aware training with full access to the original training pairs to learn the appropriate bit allocation policies, which limits its ubiquitous usage. To this end, we introduce the first on-the-fly adaptive quantization framework that accelerates the processing time from hours to seconds. We formulate the bit allocation problem with only two bit mapping modules: one to map the input image to the image-wise bit adaptation factor and one to obtain the layer-wise adaptation factors. These bit mappings are calibrated and fine-tuned using only a small number of calibration images. We achieve competitive performance with the previous adaptive quantization methods, while the processing time is accelerated by x2000. Codes are available at https://***/Cheeun/AdaBM.
The Memristor has become one of the best devices for hardware realization of neuralnetworks due to its nanometer size, low power consumption, fast reading and writing speed, and variable resistance. This paper propos...
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