Due to object detection's close relationship with video analysis and image understanding, it has attracted much research attention in recent years. Traditional object detection methods are built on handcrafted features and shallow trainable architectures. Their performance easily stagnates by constructing complex ensembles which combine multiple low-level image features with high-level context from object detectors and scene classifiers. With the rapid development in deep learning, more powerful tools, which are able to learn semantic, high-level, deeper features, are introduced to address the problems existing in traditional architectures. These models behave differently in network architecture, training strategy and optimization function, etc. In this paper, we provide a review on deep learning based object detection frameworks. Our review begins with a brief introduction on the history of deep learning and its representative tool, namely Convolutional Neural Network (CNN). Then we focus on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further. As distinct specific detection tasks exhibit different characteristics, we also briefly survey several specific tasks, including salient object detection, face detection and pedestrian detection. Experimental analyses are also provided to compare various methods and draw some meaningful conclusions. Finally, several promising directions and tasks are provided to serve as guidelines for future work in both object detection and relevant neural network based learning systems.
Deep learning has recently become one of the most popular sub-fields of machine learning owing to its distributed data representation with multiple levels of abstraction. A diverse range of deep learning algorithms are being employed to solve conventional artificial intelligence problems. This paper gives an overview of some of the most widely used deep learning algorithms applied in the field of computer vision. It first inspects the various approaches of deep learning algorithms, followed by a description of their applications in image classification, object identification, image extraction …
Deep Learning on 3D Semantic Segmentation: A Detailed Review
National Technical University of Athens (NTUA)（雅典国立技术大学）；Université de Strasbourg（斯特拉斯堡大学）；INSA Strasbourg（斯特拉斯堡国立应用科学学院）；CNRS（法国国家科学研究中心）；ICube Laboratory（ICube实验室）
University of Science and Technology of China（中国科学技术大学）；Institute of Intelligent Machines, Chinese Academy of Sciences（中国科学院智能机械研究所）
Recent years have witnessed the remarkable developments made by deep learning techniques for object detection, a fundamentally challenging problem of computer vision. Nevertheless, there are still difficulties in training accurate deep object detectors, one of which is owing to the foreground-background imbalance problem. In this paper, we survey the recent advances about the solutions to the imbalance problem. First, we analyze the characteristics of the imbalance problem in different kinds of deep detectors, including one-stage and two-stage ones. Second, we divide the existing solutions int…
Deep learning (DL) algorithms are considered as a methodology of choice for remote-sensing image analysis over the past few years. Due to its effective applications, deep learning has also been introduced for automatic change detection and achieved great success. The present study attempts to provide a comprehensive review and a meta-analysis of the recent progress in this subfield. Specifically, we first introduce the fundamentals of deep learning methods which arefrequently adopted for change detection. Secondly, we present the details of the meta-analysis conducted to examine the status of …
Many research works focus on leveraging the complementary geometric information of indoor depth sensors in vision tasks performed by deep convolutional neural networks, notably semantic segmentation. These works deal with a specific vision task known as "RGB-D Indoor Semantic Segmentation". The challenges and resulting solutions of this task differ from its standard RGB counterpart. This results in a new active research topic. The objective of this paper is to introduce the field of Deep Convolutional Neural Networks for RGB-D Indoor Semantic Segmentation. This review presents the most popular…
In this paper, we present a comprehensive review of the imbalance problems in object detection. To analyze the problems in a systematic manner, we introduce a problem-based taxonomy. Following this taxonomy, we discuss each problem in depth and present a unifying yet critical perspective on the solutions in the literature. In addition, we identify major open issues regarding the existing imbalance problems as well as imbalance problems that have not been discussed before. Moreover, in order to keep our review up to date, we provide an accompanying webpage which catalogs papers addressing imbal…
Convolutional Neural Network (CNN) has become the state-of-the-art for object detection in image task. In this chapter, we have explained different state-of-the-art CNN based object detection models. We have made this review with categorization those detection models according to two different approaches: two-stage approach and one-stage approach. Through this chapter, it has shown advancements in object detection models from R-CNN to latest RefineDet. It has also discussed the model description and training details of each model. Here, we have also drawn a comparison among those models.
Textureless object recognition has become a significant task in Computer Vision with the advent of Robotics and its applications in manufacturing sector. It has been very challenging to get good performance because of its lack of discriminative features and reflectance properties. Hence, the approaches used for textured objects cannot be applied for textureless objects. A lot of work has been done in the last 20 years, especially in the recent 5 years after the TLess and other textureless dataset were introduced. In our research, we plan to combine image processing techniques (for feature enha…
Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various applicati…
College of Computer Science and Technology, Huaqiao University（华侨大学计算机科学与技术学院）；School of Economics and Finance, Huaqiao University（华侨大学经济与金融学院）
Despite the remarkable performance of deep learning methods on various tasks, most cutting-edge models rely heavily on large-scale annotated training examples, which are often unavailable for clinical and health care tasks. The labeling costs for medical images are very high, especially in medical image segmentation, which typically requires intensive pixel/voxel-wise labeling. Therefore, the strong capability of learning and generalizing from limited supervision, including a limited amount of annotations, sparse annotations, and inaccurate annotations, is crucial for the successful applicatio…
Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. It can be a key step to provide a reliable basis for clinical diagnosis, such as 3D reconstruction of human tissues, image-guided interventions, image analyzing and visualization. In this review article, deep-learning-based methods for ultrasound image segmentation are categorized into six main groups according to their architectures and training at first. Secondly, for each group, several current representative algor…
Imperial College London（伦敦帝国学院）；City, University of London（伦敦城市大学）；University of Birmingham（伯明翰大学）
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the chall…
Person re-identification has received a lot of attention from the research community in recent times. Due to its vital role in security based applications, person re-identification lies at the heart of research relevant to tracking robberies, preventing terrorist attacks and other security critical events. While the last decade has seen tremendous growth in re-id approaches, very little review literature exists to comprehend and summarize this progress. This review deals with the latest state-of-the-art deep learning based approaches for person re-identification. While the few existing re-id r…
Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background …
This review article surveys the current progresses made toward video-based anomaly detection. We address the most fundamental aspect for video anomaly detection, that is, video feature representation. Much research works have been done in finding the right representation to perform anomaly detection in video streams accurately with an acceptable false alarm rate. However, this is very challenging due to large variations in environment and human movement, and high space-time complexity due to huge dimensionality of video data. The weakly supervised nature of deep learning algorithms can help in…
University of Wollongong（卧龙岗大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织 Data61）；University of Sydney（悉尼大学）
Classification of HEp-2 cell patterns plays a significant role in the indirect immunofluorescence test for identifying autoimmune diseases in the human body. Many automatic HEp-2 cell classification methods have been proposed in recent years, amongst which deep learning based methods have shown impressive performance. This paper provides a comprehensive review of the existing deep learning based HEp-2 cell image classification methods. These methods perform HEp-2 image classification at two levels, namely, cell-level and specimen-level. Both levels are covered in this review. At each level, th…
Tsinghua University（清华大学）；University of Rochester（罗切斯特大学）；University College London（伦敦大学学院）
Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effectiv…
Surface cracks are a very common indicator of potential structural faults. Their early detection and monitoring is an important factor in structural health monitoring. Left untreated, they can grow in size over time and require expensive repairs or maintenance. With recent advances in computer vision and deep learning algorithms, the automatic detection and segmentation of cracks for this monitoring process have become a major topic of interest. This review aims to give researchers an overview of the published work within the field of crack analysis algorithms that make use of deep learning. I…
LIACS, Leiden University（莱顿大学高级计算机科学研究所）；College of Systems Engineering, NUDT（国防科技大学系统工程学院）；Center for Machine Vision and Signal Analysis, University of Oulu（奥卢大学机器视觉与信号分析中心）
The focus of this survey is on the analysis of two modalities of multimodal deep learning: image and text. Unlike classic reviews of deep learning where monomodal image classifiers such as VGG, ResNet and Inception module are central topics, this paper will examine recent multimodal deep models and structures, including auto-encoders, generative adversarial nets and their variants. These models go beyond the simple image classifiers in which they can do uni-directional (e.g. image captioning, image generation) and bi-directional (e.g. cross-modal retrieval, visual question answering) multimoda…
School of Computer and Software Engineering, Xihua University（西华大学计算机与软件工程学院）
Image super-resolution technology is the process of obtaining high-resolution images from one or more low-resolution images. With the development of deep learning, image super-resolution technology based on deep learning method is emerging. This paper reviews the research progress of the application of depth learning method in the field of image super-resolution, introduces this kind of super-resolution work from several aspects, and looks forward to the further application of depth learning method in the field of image super-resolution. By collecting and counting the relevant literature on th…
Deep Learning Techniques for In-Crop Weed Identification: A Review
School of Computer Science, The University of Sydney（悉尼大学计算机学院）；School of Life and Environmental Sciences, The University of Sydney（悉尼大学生命与环境科学学院）；Australian Centre for Field Robotics, The University of Sydney（悉尼大学澳大利亚田间机器人中心）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；College of Mathematics and Computer Science, Wuhan Polytechnic University（武汉轻工大学数学与计算机学院）；Department of Computer Science and Engineering, Texas A&M University（德克萨斯农工大学计算机科学与工程系）；Citrus Research and Education Center, University of Florida（佛罗里达大学柑橘研究与教育中心）
Hunan First Normal University（湖南第一师范学院）；Southeast University（东南大学）；National University of Defense Technology（国防科技大学）
Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the …
Recent Deep Learning in Crowd Behaviour Analysis: A Brief Review
School of Computer Science, University of Leeds（利兹大学计算机科学学院）；UCL Centre for Artificial Intelligence, Department of Computer Science, University College London（伦敦大学学院人工智能中心、计算机科学系）
Norwegian University of Science and Technology（挪威科技大学）
Deep learning models are known to function like the human brain. Due to their functional mechanism, they are frequently utilized to accomplish tasks that require human intelligence. Multi-target tracking (MTT) for video surveillance is one of the important and challenging tasks, which has attracted the researcher's attention due to its potential applications in various domains. Multi-target tracking tasks require locating the objects individually in each frame, which remains a huge challenge as there are immediate changes in appearances and extreme occlusions of objects. In addition to that, t…
With the proliferation of the e-commerce industry, analyzing customer feedback is becoming indispensable to a service provider. In recent days, it can be noticed that customers upload the purchased product images with their review scores. In this paper, we undertake the task of analyzing such visual reviews, which is very new of its kind. In the past, the researchers worked on analyzing language feedback, but here we do not take any assistance from linguistic reviews that may be absent, since a recent trend can be observed where customers prefer to quickly upload the visual feedback instead of…
Image Captioning is a task that combines computer vision and natural language processing, where it aims to generate descriptive legends for images. It is a two-fold process relying on accurate image understanding and correct language understanding both syntactically and semantically. It is becoming increasingly difficult to keep up with the latest research and findings in the field of image captioning due to the growing amount of knowledge available on the topic. There is not, however, enough coverage of those findings in the available review papers. We perform in this paper a run-through of t…
University of Waterloo（滑铁卢大学）；Sun Yat-Sen University（中山大学）；Xilinx Technology Beijing Limited（赛灵思科技北京有限公司）；Ryerson University（瑞尔森大学）
Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3D point clouds is a challenging and tedious task. In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification. Although several published research papers focus on specific topics in computer visi…
University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）
Image captioning is the task of automatically generating sentences that describe an input image in the best way possible. The most successful techniques for automatically generating image captions have recently used attentive deep learning models. There are variations in the way deep learning models with attention are designed. In this survey, we provide a review of literature related to attentive deep learning models for image captioning. Instead of offering a comprehensive review of all prior work on deep image captioning models, we explain various types of attention mechanisms used for the …
Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Afterwards, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.
This literature review will discuss the use of deep learning methods for image reconstruction using fMRI data. More specifically, the quality of image reconstruction will be determined by the choice in decoding and reconstruction architectures. I will show that these structures can struggle with adaptability to various input stimuli due to complicated objects in images. Also, the significance of feature representation will be evaluated. This paper will conclude the use of deep learning within visual decoding and reconstruction is highly optimal when using variations of deep neural networks and…
University of Waterloo（滑铁卢大学）；Manipal Institute of Technology（马尼帕尔理工学院）
An overview of the applications of deep learning in ophthalmic diagnosis using retinal fundus images is presented. We also review various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk, blood vessels and retinal layer as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma,diabetic macular edema and diabetic retinopathy are also reported.
A Review of Deep Learning-based Approaches for Deepfake Content Detection
São Paulo State University（圣保罗州立大学）；TECNALIA（泰克纳利亚科技中心）；University of the Basque Country (UPV/EHU)（巴斯克大学（UPV/EHU））；Universidad Politécnica de Madrid（马德里理工大学）
Deep supervision, or known as 'intermediate supervision' or 'auxiliary supervision', is to add supervision at hidden layers of a neural network. This technique has been increasingly applied in deep neural network learning systems for various computer vision applications recently. There is a consensus that deep supervision helps improve neural network performance by alleviating the gradient vanishing problem, as one of the many strengths of deep supervision. Besides, in different computer vision applications, deep supervision can be applied in different ways. How to make the most use of deep su…
Skin cancer is one of the most threatening diseases worldwide. However, diagnosing skin cancer correctly is challenging. Recently, deep learning algorithms have emerged to achieve excellent performance on various tasks. Particularly, they have been applied to the skin disease diagnosis tasks. In this paper, we present a review on deep learning methods and their applications in skin disease diagnosis. We first present a brief introduction to skin diseases and image acquisition methods in dermatology, and list several publicly available skin datasets for training and testing algorithms. Then, we…
For several years, numerous attempts have been made to reduce noise and artifacts in MRI. Although there have been many successful methods to address these problems, practical implementation for clinical images is still challenging because of its complicated mechanism. Recently, deep learning received considerable attention, emerging as a machine learning approach in delivering robust MR image processing. The purpose here is therefore to explore further and review noise and artifact reduction using deep learning for MRI.
University of Leeds（利兹大学）；Istituto Italiano di Tecnologia（意大利理工学院）；University of Glasgow（格拉斯哥大学）
Touch sensing can help robots understand their sur- rounding environment, and in particular the objects they interact with. To this end, roboticists have, in the last few decades, developed several tactile sensing solutions, extensively reported in the literature. Research into interpreting the conveyed tactile information has also started to attract increasing attention in recent years. However, a comprehensive study on this topic is yet to be reported. In an effort to collect and summarize the major scientific achievements in the area, this survey extensively reviews current trends in robot …
Center for Translational Research in Neuroimaging and Data Science (TReNDS)（神经影像与数据科学转化研究中心（TReNDS））
Deep learning has become a prominent computational modeling tool in the areas of computer vision and image processing in recent years. This research comprehensively analyzes the different deep-learning methods used for image-to-image translation and reconstruction in the natural and medical imaging domains. We examine the famous deep learning frameworks, such as convolutional neural networks and generative adversarial networks, and their variants, delving into the fundamental principles and difficulties of each. In the field of natural computer vision, we investigate the development and extens…
Multi-task learning (MTL) optimizes several learning tasks simultaneously and leverages their shared information to improve generalization and the prediction of the model for each task. Auxiliary tasks can be added to the main task to ultimately boost the performance. In this paper, we provide a brief review on the recent deep multi-task learning (dMTL) approaches followed by methods on selecting useful auxiliary tasks that can be used in dMTL to improve the performance of the model for the main task.
Indian Institute of Technology Indore（印度理工学院印多尔分校）；Nanyang Technological University（南洋理工大学）；Qatar University（卡塔尔大学）
Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning architectures are showing better performance compared to the shallow or traditional models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into bagging, boosting, stacking, nega…
Recommender Systems are tools that improve how users find relevant information in web systems, so they do not face too much information. In order to generate better recommendations, the context of information should be used in the recommendation process. Context-Aware Recommender Systems were created, accomplishing state-of-the-art results and improving traditional recommender systems. There are many approaches to build recommender systems, and two of the most prominent advances in area have been the use of Embeddings to represent the data in the recommender system, and the use of Deep Learnin…
Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；Islamic Azad University, Mashhad Branch（伊斯兰阿扎德大学马什哈德分校）
Lung cancer has been one of the most prevalent disease in recent years. According to the research of this field, more than 200,000 cases are identified each year in the US. Uncontrolled multiplication and growth of the lung cells result in malignant tumour formation. Recently, deep learning algorithms, especially Convolutional Neural Networks (CNN), have become a superior way to automatically diagnose disease. The purpose of this article is to review different models that lead to different accuracy and sensitivity in the diagnosis of early-stage lung cancer and to help physicians and researche…
Lanzhou University of Technology（兰州理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；University of Glasgow（格拉斯哥大学）；University College London（伦敦大学学院）
Few-shot image classification is a challenging problem that aims to achieve the human level of recognition based only on a small number of training images. One main solution to few-shot image classification is deep metric learning. These methods, by classifying unseen samples according to their distances to few seen samples in an embedding space learned by powerful deep neural networks, can avoid overfitting to few training images in few-shot image classification and have achieved the state-of-the-art performance. In this paper, we provide an up-to-date review of deep metric learning methods f…
Gour Mahavidyalaya（古尔马哈维迪亚拉亚学院）；University of Gour Banga（古尔班加大学）；Visva-Bharati University（维斯瓦巴拉蒂大学）；National Research Council of Italy（意大利国家研究委员会）；Institute of Applied Sciences and Intelligent Systems（应用科学与智能系统研究所）
Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly disc…
Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review
Richard and Loan Hill Department of Biomedical Engineering, University of Illinois at Chicago（伊利诺伊大学芝加哥分校Richard and Loan Hill生物医学工程系）；Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago（伊利诺伊大学芝加哥分校眼科人工智能中心）
Deep generative models are a class of techniques that train deep neural networks to model the distribution of training samples. Research has fragmented into various interconnected approaches, each of which make trade-offs including run-time, diversity, and architectural restrictions. In particular, this compendium covers energy-based models, variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, in addition to numerous hybrid approaches. These techniques are compared and contrasted, explaining the premises behind each and how they are interrelated,…
Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques
University of Auckland（奥克兰大学）；Auckland Bioengineering Institute（奥克兰生物工程研究所）
Shiraz University of Medical Sciences（设拉子医科大学）；University of Pittsburgh School of Medicine（匹兹堡大学医学院）；UPMC Hillman Cancer Center（UPMC希尔曼癌症中心）；Shiraz University of Technology（设拉子理工大学）；Mashhad University of Medical Science（马什哈德医科大学）
In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration in this area. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy, playing a key role in countering tumors during the operation. The current study presents a comprehe…
Nankai University（南开大学）；Beijing Tongren Hospital, Capital Medical University（首都医科大学附属北京同仁医院）；Inception Institute of Artificial Intelligence（因ception人工智能研究院）
The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very necessary to summarize the recent developments in deep learning for fundus images with a review paper. In this review, we introduce 143 application papers with a carefully designed hierarchy. Moreover, 33 publicly available datasets are presented. Summaries and analyses are provid…
Shahrood University of Technology（沙赫鲁德工业大学）；Faculty of Electrical & Robotics Engineering（电气与机器人工程学院）
Nowadays, Deep Neural Networks are among the main tools used in various sciences. Convolutional Neural Network is a special type of DNN consisting of several convolution layers, each followed by an activation function and a pooling layer. The pooling layer is an important layer that executes the down-sampling on the feature maps coming from the previous layer and produces new feature maps with a condensed resolution. This layer drastically reduces the spatial dimension of input. It serves two main purposes. The first is to reduce the number of parameters or weights, thus lessening the computat…
With the recent advances in hardware and rendering techniques, 3D models have emerged everywhere in our life. Yet creating 3D shapes is arduous and requires significant professional knowledge. Meanwhile, Deep learning has enabled high-quality 3D shape reconstruction from various sources, making it a viable approach to acquiring 3D shapes with minimal effort. Importantly, to be used in common 3D applications, the reconstructed shapes need to be represented as polygonal meshes, which is a challenge for neural networks due to the irregularity of mesh tessellations. In this survey, we provide a co…
University of Adelaide（阿德莱德大学）；University of Technology Sydney（悉尼科技大学）
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intui…
