• Publications
  • Influence
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
TLDR
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. Expand
Real-time human activity recognition from accelerometer data using Convolutional Neural Networks
TLDR
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. Expand
AI Benchmark: Running Deep Neural Networks on Android Smartphones
TLDR
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. Expand
AI Benchmark: All About Deep Learning on Smartphones in 2019
TLDR
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. Expand
Replacing Mobile Camera ISP with a Single Deep Learning Model
TLDR
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. Expand
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
TLDR
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. Expand
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
TLDR
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. Expand
Rendering Natural Camera Bokeh Effect with Deep Learning
TLDR
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. Expand
Human activity recognition using quasiperiodic time series collected from a single tri-axial accelerometer
TLDR
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. Expand
AIM 2019 Challenge on Bokeh Effect Synthesis: Methods and Results
TLDR
This paper reviews the first AIM challenge on bokeh effect synthesis with the focus on proposed solutions and results, defining the state-of-the-art for practical bokeH effect simulation. Expand
...
1
2
3
...