This article presents an artificial intelligence model capable of identifying actions strongly related to trichotillomania, a psychiatric disorder that causes people to have a desire to pull their hair. The model was ...
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Frame interpolation is an essential video processing technique that adjusts the temporal resolution of an image sequence. While deep learning has brought great improvements to the area of video frame interpolation, te...
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ISBN:
(纸本)9781665493468
Frame interpolation is an essential video processing technique that adjusts the temporal resolution of an image sequence. While deep learning has brought great improvements to the area of video frame interpolation, techniques that make use of neuralnetworks can typically not easily be deployed in practical applications like a video editor since they are either computationally too demanding or fail at high resolutions. In contrast, we propose a deep learning approach that solely relies on splatting to synthesize interpolated frames. This splatting-based synthesis for video frame interpolation is not only much faster than similar approaches, especially for multi-frame interpolation, but can also yield new state-of-the-art results at high resolutions.
Over the last few years, there has been a great deal of progress in the field of image recognition, using classical or modern methods based on machinelearning algorithms. In this context, numerous studies on object d...
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Spiking neuralnetworks (SNNs) are bioplausible machinelearning models that use discrete spikes to encode, compute, and transmit information. Combined with event-driven low-power hardware, SNNs can improve the energy...
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ISBN:
(纸本)9798331530082;9798331530075
Spiking neuralnetworks (SNNs) are bioplausible machinelearning models that use discrete spikes to encode, compute, and transmit information. Combined with event-driven low-power hardware, SNNs can improve the energy efficiency of learning tasks. Although there have been several efforts to build SNN hardware, there is no uniform framework to verify and benchmark these designs in terms of key hardware performance metrics such as inference accuracy, area, power consumption, and throughput. We propose PRONTO, an open-source and extensible framework to verify SNN hardware for different learning tasks and datasets. Given the ubiquity of PyTorch in the machinelearning community and for demonstration purposes, the frontend of PRONTO is integrated with a torch-based SNN simulator for model specification and training. Its backend is integrated with an open-source quantized SNN hardware. PRONTO interfaces with a torch code to generate input stimuli which are then driven to SNN hardware through a configurable SystemVerilog testbench, verifying the design across various SNN-specific configurations. PRONTO utilizes a dataflow-based approach to validate SNN models that are segmented and run on a mix of software and hardware platforms. We describe PRONTO and evaluate it using six datasets spanning image, audio, and text classification. We present benchmark results for various input settings. PRONTO is available under an open-source licensing to provide a platform to evaluate all current and future SNN hardware designs. We believe PRONTO will substantially reduce the design verification effort, thus facilitating fast design prototyping.
Real-time imageprocessing is a key area of focus, but computationally intensive. neuralnetworks effectively address classification tasks, but they are not always a viable option, particularly in environments where h...
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ISBN:
(纸本)9781510673199;9781510673182
Real-time imageprocessing is a key area of focus, but computationally intensive. neuralnetworks effectively address classification tasks, but they are not always a viable option, particularly in environments where high power consumption or computational requirements are limiting factors. Hardware devices such as Field-Programmable Gate Arrays (FPGAs) offer significant parallelization capabilities that can be fully exploited when the implemented circuit is composed solely of logic gates. In addition, FPGAs are also interesting alternatives to traditional GPU-based implementations in terms of power consumption and reconfiguration capabilities. They can be used as a demonstration platform to validate a hardware design that can be later manufactured, creating the final Application-Specific Integrated Circuit (ASIC). This paper introduces a practical demonstration platform based on an FPGA that highlights the great capabilities of logic neuralnetworks, a type of neural network constructed exclusively with logic gates. By harnessing FPGA parallelization and logic gates, we have achieved a balance between computational power and real-time performance. This approach ensures that image classification occurs at speeds on the order of nanoseconds. This ultra-fast processing is well-suited for real-time image analysis applications across various domains. Industries that rely on quality control, such as manufacturing, will benefit from rapid and precise assessments. In the field of medical imageprocessing, where quick diagnoses are crucial, this technology promises transformative advancements. The demonstration platform developed serves as a proof of concept for logic neuralnetworks, offering a solution to the challenge of real-time imageprocessing and representing the first step towards the implementation of future architectures of logic networks in hardware.
The current deep learning-based target detection algorithms have problems such as the network perception domain being limited, poor adaptation to scale changes, feature mismatch in feature fusion, and small datasets. ...
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ISBN:
(纸本)9798350350920
The current deep learning-based target detection algorithms have problems such as the network perception domain being limited, poor adaptation to scale changes, feature mismatch in feature fusion, and small datasets. Aiming at the current problems in the field of infrared target detection, a global infrared image detection method based on graph convolutional neural network is proposed. In this paper, global feature interaction module and feature pyramid module are designed. It also proposes a graph-based knowledge distillation model compression method to provide support for hardware deployment. Finally, the algorithm proposed in this paper is experimentally demonstrated, using the classical infrared small target dataset for experiments, comparing the mainstream infrared small target detection algorithms, comparing and verifying that the algorithm of this paper has an effective performance enhancement in infrared small targets in infrared targets. Design ablation experiments to verify the performance of individual modules and fusion modules[2], to prove the effectiveness and enhancement of the module. Finally, the visualization analysis facilitates the subjective evaluation by the human eye, proving the excellence of this paper's algorithm.
With the continuous progress of machinelearning in imageprocessing, artificial neuralnetworks have more and more applications in medical imageprocessing. Aiming at the method of selecting a BP neural network to re...
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The paper examines the integration of AI in visual communication design, particularly focusing on the enhanced impact of images when combined with text. It explores the evolution of visual communication design in the ...
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image Super-Resolution (SR) is a vital imageprocessing technique aimed at improving the resolution of lowresolution images to generate high-resolution images, which is essential in applications such as medical imagin...
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In recent years, deep learning has achieved remarkable success in various fields, including biology and medicine. However, the interpretability and robustness still face challenges, as erroneous predictions can lead t...
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