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- Real-time human activity recognition from accelerometer data using Convolutional Neural Networks815
A user-independent deep learning-based approach for online human activity classification using Convolutional Neural Networks for local feature extraction together with simple statistical features that preserve information about the global form of time series is presented.
- DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks594
An end-to-end deep learning approach that bridges the gap by translating ordinary photos into DSLR-quality images by learning the translation function using a residual convolutional neural network that improves both color rendition and image sharpness.
- AI Benchmark: Running Deep Neural Networks on Android Smartphones377
A study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones, as well as an overview of the hardware acceleration resources available on four main mobile chipset platforms.
- AI Benchmark: All About Deep Learning on Smartphones in 2019267
This paper evaluates the performance and compares the results of all chipsets from Qualcomm, HiSilicon, Samsung, MediaTek and Unisoc that are providing hardware acceleration for AI inference and discusses the recent changes in the Android ML pipeline.
- Replacing Mobile Camera ISP with a Single Deep Learning Model223
PyNET is presented, a novel pyramidal CNN architecture designed for fine-grained image restoration that implicitly learns to perform all ISP steps such as image demosaicing, denoising, white balancing, color and contrast correction, demoireing, etc.
- WESPE: Weakly Supervised Photo Enhancer for Digital Cameras219
This work introduces a weakly supervised photo enhancer (WESPE) - a novel image-to-image Generative Adversarial Network-based architecture that produces comparable or improved qualitative results with state-of-the-art strongly supervised methods.
- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
- Rendering Natural Camera Bokeh Effect with Deep Learning100
This paper presents a large-scale bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR with 50mm f/1.8 lenses, and proposes to learn a realistic shallow focus technique directly from the photos produced by DSLR cameras.
- AIM 2020 Challenge on Learned Image Signal Processing Pipeline90
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results, defining the state-of-the-art for practical image signal processing pipeline modeling.
- Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer88
This paper proposes a method for human physical activity recognition using time series, collected from a single tri-axial accelerometer of a smartphone, and achieves high precision, ensuring nearly 96 % recognition accuracy when using the bunch of segmentation and k-nearest neighbor algorithms.
- Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report76
The first Mobile AI challenge is introduced, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a real-time performance on mobile or edge NPUs.
- Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report70
The target was to develop an end-to-end deep learning-based image signal processing (ISP) pipeline that can replace classical hand-crafted ISPs and achieve nearly real-time performance on smartphone NPUs.
- Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report69
The target is to develop an end-to-end deep learning-based video super-resolution solutions that can achieve a realtime performance on mobile GPUs and can upscale videos to HD resolution at up to 80 FPS while demonstrating high fidelity results.
- AIM 2020 Challenge on Rendering Realistic Bokeh52
The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.
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- Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report39
This paper introduces the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based depth estimation solutions that can demonstrate a nearly realtime performance on smartphones and IoT platforms.
- Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report39
The first Mobile AI challenge is introduced, where the target is to develop an end-to-end deep learning-based image denoising solution that can demonstrate high efficiency on smartphone GPUs.
- A Large-Scale CNN Ensemble for Medication Safety Analysis36
This paper proposes an end-to-end CNN-based method for predicting drug safety on user comments from healthcare discussion forums based on a vast ensemble of CNNs with varied structural parameters, where the prediction is determined by the majority vote.
- PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks30
The results demonstrate that the PyNET-V2 Mobile model can substantially surpass the quality of tradition ISP pipelines, while outperforming the previously introduced neural network-based solutions designed for fast image processing.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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