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A review of android malware detection approaches based on machine learning
K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are develo** rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …
malware is also emerging in an endless stream. Many researchers have studied the …
On the synergies between machine learning and binocular stereo for depth estimation from images: A survey
Stereo matching is one of the longest-standing problems in computer vision with close to 40
years of studies and research. Throughout the years the paradigm has shifted from local …
years of studies and research. Throughout the years the paradigm has shifted from local …
Uncertainty estimation for stereo matching based on evidential deep learning
Although deep learning-based stereo matching approaches have achieved excellent
performance in recent years, it is still a non-trivial task to estimate the uncertainty of the …
performance in recent years, it is still a non-trivial task to estimate the uncertainty of the …
A survey on deep learning techniques for stereo-based depth estimation
Estimating depth from RGB images is a long-standing ill-posed problem, which has been
explored for decades by the computer vision, graphics, and machine learning communities …
explored for decades by the computer vision, graphics, and machine learning communities …
On the uncertainty of self-supervised monocular depth estimation
Self-supervised paradigms for monocular depth estimation are very appealing since they do
not require ground truth annotations at all. Despite the astonishing results yielded by such …
not require ground truth annotations at all. Despite the astonishing results yielded by such …
Real-time self-adaptive deep stereo
Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to
regress dense disparity maps from stereo pairs. These models, however, suffer from a …
regress dense disparity maps from stereo pairs. These models, however, suffer from a …
Learning monocular depth estimation infusing traditional stereo knowledge
Depth estimation from a single image represents a fascinating, yet challenging problem with
countless applications. Recent works proved that this task could be learned without direct …
countless applications. Recent works proved that this task could be learned without direct …
Harnessing GPU tensor cores for fast FP16 arithmetic to speed up mixed-precision iterative refinement solvers
Low-precision floating-point arithmetic is a powerful tool for accelerating scientific computing
applications, especially those in artificial intelligence. Here, we present an investigation …
applications, especially those in artificial intelligence. Here, we present an investigation …
Neural disparity refinement
We propose a framework that combines traditional, hand-crafted algorithms and recent
advances in deep learning to obtain high-quality, high-resolution disparity maps from stereo …
advances in deep learning to obtain high-quality, high-resolution disparity maps from stereo …
Guided stereo matching
Stereo is a prominent technique to infer dense depth maps from images, and deep learning
further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when …
further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when …