PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks

Ignatov, Andrey; Malivenko, Grigory; Timofte, Radu; Tseng, Yu; Xu, Yulong; Yu, Po-Hsiang; Chiang, Cheng-Ming; Kuo, Hsien-Kai; Min-Hung, Chen,; Cheng, Chia-Ming; Gool, Luc Van

doi:10.1109/icpr56361.2022.9956598

Cited by 17 publications

(5 citation statements)

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“…If not, a custom model is needed, which can be trained and adjusted to the application's needs on a computer or in the cloud. The pretrained model can be a common model used for a specific purpose or tailored to research needs, such as in [21][22][23][24][25][26].…”

Section: On-device Testing With Pretrained Modelsmentioning

confidence: 99%

Analysis of Machine Learning Techniques for Information Classification in Mobile Applications

2023

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Due to the daily use of mobile technologies, we live in constant connection with the world through the Internet. Technological innovations in smart devices have allowed us to carry out everyday activities such as communicating, working, studying or using them as a means of entertainment, which has led to smartphones displacing computers as the most important device connected to the Internet today, causing users to demand smarter applications or functionalities that allow them to meet their needs. Artificial intelligence has been a major innovation in information technology that is transforming the way users use smart devices. Using applications that make use of artificial intelligence has revolutionised our lives, from making predictions of possible words based on typing in a text box, to being able to unlock devices through pattern recognition. However, these technologies face problems such as overheating and battery drain due to high resource consumption, low computational capacity, memory limitations, etc. This paper reviews the most important artificial intelligence algorithms for mobile devices, emphasising the challenges and problems that can arise when implementing these technologies in low-resource devices.

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Section: On-device Testing With Pretrained Modelsmentioning

confidence: 99%

Analysis of Machine Learning Techniques for Information Classification in Mobile Applications

2023

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“…-Image Super-Resolution: [43,63,9,73] -Learned End-to-End ISP: [40,44,35,30] -Perceptual Image Enhancement: [43,36] -Bokeh Effect Rendering: [33,42] -Image Denoising: […”

Section: Additional Literaturementioning

confidence: 99%

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

Ignatov¹,

Timofte²,

Denna³

et al. 2022

Preprint

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Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.

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“…-Learned End-to-End ISP: [36,40,30,26] -Perceptual Image Enhancement: [39,31] -Image Super-Resolution: [39,62,6,70] -Bokeh Effect Rendering: [28,38] -Image Denoising: […”

Section: Additional Literaturementioning

confidence: 99%

Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report

Ignatov¹,

Malivenko²,

Timofte³

et al. 2022

Preprint

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Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.

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PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks

Cited by 17 publications

References 50 publications

Analysis of Machine Learning Techniques for Information Classification in Mobile Applications

Analysis of Machine Learning Techniques for Information Classification in Mobile Applications

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report

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