Feature extraction
View article: Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning Open
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchica…
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Reading digits in natural images with unsupervised feature learning Open
Detecting and reading text from natural images is a hard computer vision task that is central to a variety of emerging applications. Related problems like document character recognition have been widely studied by computer vision and machi…
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Predicting the Future — Big Data, Machine Learning, and Clinical Medicine Open
The algorithms of machine learning, which can sift through vast numbers of variables looking for combinations that reliably predict outcomes, will improve prognosis, displace much of the work of radiologists and anatomical pathologists, an…
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Road Extraction by Deep Residual U-Net Open
Road extraction from aerial images has been a hot research topic in the field\nof remote sensing image analysis. In this letter, a semantic segmentation\nneural network which combines the strengths of residual learning and U-Net is\npropos…
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Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks Open
Over the last decade, Convolutional Neural Network (CNN) models have been\nhighly successful in solving complex vision problems. However, these deep\nmodels are perceived as "black box" methods considering the lack of\nunderstanding of the…
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VoxCeleb2: Deep Speaker Recognition Open
The objective of this paper is speaker recognition under noisy and\nunconstrained conditions.\n We make two key contributions. First, we introduce a very large-scale\naudio-visual speaker recognition dataset collected from open-source medi…
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Graph-based approach for airborne light detection and ranging segmentation Open
We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, gener…
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Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning Open
We present a new dataset of image caption annotations, Conceptual Captions, which contains an order of magnitude more images than the MS-COCO dataset (Lin et al., 2014) and represents a wider variety of both images and image caption styles…
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OpenFace: An open source facial behavior analysis toolkit Open
Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace - an open source tool intended for computer vision and machine learning researchers, affective compu…
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HybridSN: Exploring 3-D–2-D CNN Feature Hierarchy for Hyperspectral Image Classification Open
Hyperspectral image (HSI) classification is widely used for the analysis of\nremotely sensed images. Hyperspectral imagery includes varying bands of images.\nConvolutional Neural Network (CNN) is one of the most frequently used deep\nlearn…
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Unsupervised Representation Learning by Predicting Image Rotations Open
Over the last years, deep convolutional neural networks (ConvNets) have transformed the field of computer vision thanks to their unparalleled capacity to learn high level semantic image features. However, in order to successfully learn tho…
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Deep Learning Applications in Medical Image Analysis Open
The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine …
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Efficient Multi-Scale Attention Module with Cross-Spatial Learning Open
Remarkable effectiveness of the channel or spatial attention mechanisms for producing more discernible feature representation are illustrated in various computer vision tasks. However, modeling the cross-channel relationships with channel …
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Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package) Open
Time series feature engineering is a time-consuming process because scientists and engineers have to consider the multifarious algorithms of signal processing and time series analysis for identifying and extracting meaningful features from…
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Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook's Explanations Open
International audience
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SpectralFormer: Rethinking Hyperspectral Image Classification with\n Transformers Open
Hyperspectral (HS) images are characterized by approximately contiguous\nspectral information, enabling the fine identification of materials by\ncapturing subtle spectral discrepancies. Owing to their excellent locally\ncontextual modeling…
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Classification using deep learning neural networks for brain tumors Open
Deep Learning is a new machine learning field that gained a lot of interest over the past few years. It was widely applied to several applications and proven to be a powerful machine learning tool for many of the complex problems. In this …
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EANN Open
As news reading on social media becomes more and more popular, fake news becomes a major issue concerning the public and government. The fake news can take advantage of multimedia content to mislead readers and get dissemination, which can…
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Classification of breast cancer histology images using Convolutional Neural Networks Open
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis syst…
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EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos Open
Surgical workflow recognition has numerous potential medical applications, such as the automatic indexing of surgical video databases and the optimization of real-time operating room scheduling, among others. As a result, surgical phase re…
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R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object Open
Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist…
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DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks Open
This paper studies monocular visual odometry (VO) problem. Most of existing VO algorithms are developed under a standard pipeline including feature extraction, feature matching, motion estimation, local optimisation, etc. Although some of …
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A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction Open
Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many m…
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Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection Open
Recent advances on 3D object detection heavily rely on how the 3D data are represented, i.e., voxel-based or point-based representation. Many existing high performance 3D detectors are point-based because this structure can better retain p…
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M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network Open
Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask RCNN, DetNet) to alleviate the problem arising from scale varia…
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Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification Open
Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic …
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Plant Disease Detection and Classification by Deep Learning—A Review Open
Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and …
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Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox Open
This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately de…
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Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks Open
In this paper, we focus on tackling the problem of automatic accurate localization of detected objects in high-resolution remote sensing images. The two major problems for object localization in remote sensing images caused by the complex …
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LSTM-CNN Architecture for Human Activity Recognition Open
In the past years, traditional pattern recognition methods have made great progress. However, these methods rely heavily on manual feature extraction, which may hinder the generalization model performance. With the increasing popularity an…