• arXiv.cs.CV Pub Date : 2020-04-05
Ali Borji

Object detection remains as one of the most notorious open problems in computer vision. Despite large strides in accuracy in recent years, modern object detectors have started to saturate on popular benchmarks raising the question of how far we can reach with deep learning tools and tricks. Here, by employing 2 state-of-the-art object detection benchmarks, and analyzing more than 15 models over 4 large

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Seyed Mojtaba Marvasti-Zadeh; Hossein Ghanei-Yakhdan; Shohreh Kasaei

In recent years, the background-aware correlation filters have achie-ved a lot of research interest in the visual target tracking. However, these methods cannot suitably model the target appearance due to the exploitation of hand-crafted features. On the other hand, the recent deep learning-based visual tracking methods have provided a competitive performance along with extensive computations. In this

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Seyed Mojtaba Marvasti-Zadeh; Hossein Ghanei-Yakhdan; Shohreh Kasaei

In recent years, visual tracking methods that are based on discriminative correlation filters (DCF) have been very promising. However, most of these methods suffer from a lack of robust scale estimation skills. Although a wide range of recent DCF-based methods exploit the features that are extracted from deep convolutional neural networks (CNNs) in their translation model, the scale of the visual target

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Steven A. Grosz; Tarang Chugh; Anil K. Jain

The vulnerability of automated fingerprint recognition systems to presentation attacks (PA), i.e., spoof or altered fingers, has been a growing concern, warranting the development of accurate and efficient presentation attack detection (PAD) methods. However, one major limitation of the existing PAD solutions is their poor generalization to new PA materials and fingerprint sensors, not used in training

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Yinan Zhao; Brian Price; Scott Cohen; Danna Gurari

While deep convolutional neural networks have led to great progress in image semantic segmentation, they typically require collecting a large number of densely-annotated images for training. Moreover, once trained, the model can only make predictions in a pre-defined set of categories. Therefore, few-shot image semantic segmentation has been explored to learn to segment from only a few annotated examples

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06

Blind image deblurring remains a challenging problem for modern artificial neural networks. Unlike other image restoration problems, deblurring networks fail behind the performance of existing deblurring algorithms in case of uniform and 3D blur models. This follows from the diverse and profound effect that the unknown blur-kernel has on the deblurring operator. We propose a new architecture which

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Hanxiao Liu; Andrew Brock; Karen Simonyan; Quoc V. Le

Normalization layers and activation functions are critical components in deep neural networks that frequently co-locate with each other. Instead of designing them separately, we unify them into a single computation graph, and evolve its structure starting from low-level primitives. Our layer search algorithm leads to the discovery of EvoNorms, a set of new normalization-activation layers that go beyond

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Abhinav Kumar; Tim K. Marks; Wenxuan Mou; Ye Wang; Michael Jones; Anoop Cherian; Toshiaki Koike-Akino; Xiaoming Liu; Chen Feng

Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting landmark locations, associated uncertainties of these predicted locations, and landmark visibilities. We

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Qiuyu Chen; Wei Zhang; Ning Zhou; Peng Lei; Yi Xu; Yu Zheng; Jianping Fan

To leverage deep learning for image aesthetics assessment, one critical but unsolved issue is how to seamlessly incorporate the information of image aspect ratios to learn more robust models. In this paper, an adaptive fractional dilated convolution (AFDC), which is aspect-ratio-embedded, composition-preserving and parameter-free, is developed to tackle this issue natively in convolutional kernel level

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Hannah Kerner; Catherine Nakalembe; Inbal Becker-Reshef

Accurate crop type maps provide critical information for ensuring food security, yet there has been limited research on crop type classification for smallholder agriculture, particularly in sub-Saharan Africa where risk of food insecurity is highest. Publicly-available ground-truth data such as the newly-released training dataset of crop types in Kenya (Radiant MLHub) are catalyzing this research,

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Matheus Gadelha; Giorgio Gori; Duygu Ceylan; Radomir Mech; Nathan Carr; Tamy Boubekeur; Rui Wang; Subhransu Maji

We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations. Our model can generate handle sets with varying cardinality and different types of handles (Figure 1). Key to our approach is a deep architecture that predicts both the

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Simon Graham; David Epstein; Nasir Rajpoot

Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in data hungry models that learn independent features at each orientation. Allowing CNNs to be rotation-equivariant removes the necessity to learn this set

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Xin Li; Dongxiao Zhu

With the increasing demand for millions of COVID-19 tests, Computed Tomography (CT) based test has emerged as a promising alternative to the gold standard RT-PCR test. However, it is primarily provided in emergency department and hospital settings due to the need for expensive equipment and trained radiologists. The accurate, rapid yet inexpensive test that is suitable for population screening of COVID-19

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Yu Yao; Xizi Wang; Mingze Xu; Zelin Pu; Ella Atkins; David Crandall

Video anomaly detection (VAD) has been extensively studied. However, research on egocentric traffic videos with dynamic scenes lacks large-scale benchmark datasets as well as effective evaluation metrics. This paper proposes traffic anomaly detection with a \textit{when-where-what} pipeline to detect, localize, and recognize anomalous events from egocentric videos. We introduce a new dataset called

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Sukesh Adiga V; Jose Dolz; Herve Lombaert

Segmentation using deep learning has shown promising directions in medical imaging as it aids in the analysis and diagnosis of diseases. Nevertheless, a main drawback of deep models is that they require a large amount of pixel-level labels, which are laborious and expensive to obtain. To mitigate this problem, weakly supervised learning has emerged as an efficient alternative, which employs image-level

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Kai Zhang; Jiaxin Xie; Noah Snavely; Qifeng Chen

Depth sensing is a critical component of autonomous driving technologies, but today's LiDAR- or stereo camera-based solutions have limited range. We seek to increase the maximum range of self-driving vehicles' depth perception modules for the sake of better safety. To that end, we propose a novel three-camera system that utilizes small field of view cameras. Our system, along with our novel algorithm

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Jingjing Chen; Jichao Zhang; Jiayuan Fan; Tao Chen; Enver Sangineto; Nicu Sebe

Gaze redirection aims at manipulating a given eye gaze to a desirable direction according to a reference angle and it can be applied to many real life scenarios, such as video-conferencing or taking groups. However, the previous works suffer from two limitations: (1) low-quality generation and (2) low redirection precision. To this end, we propose an innovative MultiModal-Guided Gaze Redirection~(MGGR)

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Kai Zhang; Vítor Albiero; Kevin W. Bowyer

Web-scraped, in-the-wild datasets have become the norm in face recognition research. The numbers of subjects and images acquired in web-scraped datasets are usually very large, with number of images on the millions scale. A variety of issues occur when collecting a dataset in-the-wild, including images with the wrong identity label, duplicate images, duplicate subjects and variation in quality. With

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Rui Qian; Divyansh Garg; Yan Wang; Yurong You; Serge Belongie; Bharath Hariharan; Mark Campbell; Kilian Q. Weinberger; Wei-Lun Chao

Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Qinghai Zheng; Jihua Zhu; Haoyu Tang; Xinyuan Liu; Zhongyu Li; Huimin Lu

Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labeled instances. Different from single-label and multi-label annotations, label distributions describe the instance by multiple labels with different intensities and accommodates to more general conditions. As most existing machine learning datasets merely provide logical labels

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Yuxia Geng; Jiaoyan Chen; Zhuo Chen; Zhiquan Ye; Zonggang Yuan; Yantao Jia; Huajun Chen

Zero-shot learning (ZSL) is to handle the prediction of those unseen classes that have no labeled training data. Recently, generative methods like Generative Adversarial Networks (GANs) are being widely investigated for ZSL due to their high accuracy, generalization capability and so on. However, the side information of classes used now is limited to text descriptions and attribute annotations, which

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Jun Ling; Han Xue; Li Song; Shuhui Yang; Rong Xie; Xiao Gu

Facial expression manipulation, as an image-to-image translation problem, aims at editing facial expression with a given condition. Previous methods edit an input image under the guidance of a discrete emotion label or absolute condition (e.g., facial action units) to possess the desired expression. However, these methods either suffer from changing condition-irrelevant regions or are inefficient to

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Jian Ren; Menglei Chai; Sergey Tulyakov; Chen Fang; Xiaohui Shen; Jianchao Yang

In this paper, we tackle the problem of human motion transfer, where we synthesize novel motion video for a target person that imitates the movement from a reference video. It is a video-to-video translation task in which the estimated poses are used to bridge two domains. Despite substantial progress on the topic, there exist several problems with the previous methods. First, there is a domain gap

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Zhe Wang; Daeyun Shin; Charless C. Fowlkes

Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data available is limited and it is not clear to what extent methods generalize outside the specific datasets they are trained on. In this work we carry out a systematic study of the diversity and biases present in specific datasets

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Ruturaj G. Gavaskar; Kunal N. Chaudhury

Plug-and-play (PnP) method is a recent paradigm for image regularization, where the proximal operator (associated with some given regularizer) in an iterative algorithm is replaced with a powerful denoiser. Algorithmically, this involves repeated inversion (of the forward model) and denoising until convergence. Remarkably, PnP regularization produces promising results for several restoration applications

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Yang Zhang; Changhui Hu; Xiaobo Lu

Most of current display devices are with eight or higher bit-depth. However, the quality of most multimedia tools cannot achieve this bit-depth standard for the generating images. De-quantization can improve the visual quality of low bit-depth image to display on high bit-depth screen. This paper proposes DAGAN algorithm to perform super-resolution on image intensity resolution, which is orthogonal

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Yang Zhang; Changhui Hu; Xiaobo Lu

This paper presents an novel illumination-invariant feature representation approach used to eliminate the varying illumination affection in undersampled face recognition. Firstly, a new illumination level classification technique based on Singular Value Decomposition (SVD) is proposed to judge the illumination level of input image. Secondly, we construct the logarithm edgemaps feature (LEF) based on

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Giorgio Giannone; Asha Anoosheh; Alessio Quaglino; Pierluca D'Oro; Marco Gallieri; Jonathan Masci

Event-based cameras are novel, efficient sensors inspired by the human vision system, generating an asynchronous, pixel-wise stream of data. Learning from such data is generally performed through heavy preprocessing and event integration into images. This requires buffering of possibly long sequences and can limit the response time of the inference system. In this work, we instead propose to directly

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Haitian Zeng; Haizhou Ai; Zijie Zhuang; Long Chen

Learning to predict multiple attributes of a pedestrian is a multi-task learning problem. To share feature representation between two individual task networks, conventional methods like Cross-Stitch and Sluice network learn a linear combination of features or feature subspaces. However, linear combination rules out the complex interdependency between channels. Moreover, spatial information exchanging

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Takuro Karamatsu; Gibran Benitez-Garcia; Keiji Yanai; Seiichi Uchida

In this paper, we tackle a challenging domain conversion task between photo and icon images. Although icons often originate from real object images (i.e., photographs), severe abstractions and simplifications are applied to generate icon images by professional graphic designers. Moreover, there is no one-to-one correspondence between the two domains, for this reason we cannot use it as the ground-truth

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Lisai Zhang; Qingcai Chen; Baotian Hu; Shuoran Jiang

Neural image inpainting has achieved promising performance in generating semantically plausible content. Most of the recent works mainly focus on inpainting images depending on vision information, while neglecting the semantic information implied in human languages. To acquire more semantically accurate inpainting images, this paper proposes a novel inpainting model named \textit{N}eural \textit{I}mage

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07

We, as humans, can impeccably navigate to localise a target object, even in an unseen environment. We argue that this impressive ability is largely due to incorporation of \emph{prior knowledge} (or experience) and \emph{visual cues}--that current visual navigation approaches lack. In this paper, we propose to use externally learned prior knowledge of object relations, which is integrated to our model

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Aliaksandr Siarohin*; Subhankar Roy*; Stéphane Lathuilière; Sergey Tulyakov; Elisa Ricci; Nicu Sebe

Recent co-part segmentation methods mostly operate in a supervised learning setting, which requires a large amount of annotated data for training. To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation. Differently from previous works, our approach develops the idea that motion information inferred from videos can be leveraged to discover meaningful

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Yaoyi Li; Qingyao Xu; Hongtao Lu

Natural image matting is a fundamental problem in computational photography and computer vision. Deep neural networks have seen the surge of successful methods in natural image matting in recent years. In contrast to traditional propagation-based matting methods, some top-tier deep image matting approaches tend to perform propagation in the neural network implicitly. A novel structure for more direct

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Lei Shi; Yifan Zhang; Jian Cheng; Hanqing Lu

In this paper, a new perspective is presented for skeleton-based action recognition. Specifically, we regard the skeletal sequence as a spatial-temporal point cloud and voxelize it into a 4-dimensional grid. A novel sparse 4D convolutional network (SC4D) is proposed to directly process the generated 4D grid for high-level perceptions. Without manually designing the hand-crafted transformation rules

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Geon Heo; Yuji Roh; Seonghyeon Hwang; Dayun Lee; Steven Euijong Whang

As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially critical in a manufacturing setting where smart factories rely on machine learning for product quality control by analyzing industrial images. Such images are typically large and may only need to be partially analyzed where

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Christoph Baur; Stefan Denner; Benedikt Wiestler; Shadi Albarqouni; Nassir Navab

Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these works is to learn a model of normal anatomy by learning to compress and recover healthy data. This allows to spot abnormal structures from erroneous recoveries of compressed, potentially anomalous samples. The concept is of great

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Tong ZHENG; Hirohisa ODA; Takayasu MORIYA; Takaaki SUGINO; Shota NAKAMURA; Masahiro ODA; Masaki MORI; Hirotsugu TAKABATAKE; Hiroshi NATORI; Kensaku MORI

This paper presents a super-resolution (SR) method with unpaired training dataset of clinical CT and micro CT volumes. For obtaining very detailed information such as cancer invasion from pre-operative clinical CT volumes of lung cancer patients, SR of clinical CT volumes to $\m$}CT level is desired. While most SR methods require paired low- and high- resolution images for training, it is infeasible

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Shaiq Munir Malik; Mohbat Tharani; Murtaza Taj

To solve the problem of the overwhelming size of Deep Neural Networks (DNN) several compression schemes have been proposed, one of them is teacher-student. Teacher-student tries to transfer knowledge from a complex teacher network to a simple student network. In this paper, we propose a novel method called a teacher-class network consisting of a single teacher and multiple student networks (i.e. class

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Xukun Zhang; Wenxin Hu; Wen Wu

Predicting the mutation status of genes in tumors is of great clinical significance. Recent studies have suggested that certain mutations may be noninvasively predicted by studying image features of the tumors from Computed Tomography (CT) data. Currently, this kind of image feature identification method mainly relies on manual processing to extract generalized image features alone or machine processing

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Huikai Shao; Dexing Zhong; Xuefeng Du

Deep palmprint recognition has become an emerging issue with great potential for personal authentication on handheld and wearable consumer devices. Previous studies of palmprint recognition are mainly based on constrained datasets collected by dedicated devices in controlled environments, which has to reduce the flexibility and convenience. In addition, general deep palmprint recognition algorithms

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Xiaogang Wang; Marcelo H Ang Jr; Gim Hee Lee

Point clouds are often sparse and incomplete. Existing shape completion methods are incapable of generating details of objects or learning the complex point distributions. To this end, we propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes. Considering the local details of partial input with the global shape information together, we

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Michał Koziarski

Data imbalance remains one of the open challenges in the contemporary machine learning. It is especially prevalent in case of medical data, such as histopathological images. Traditional data-level approaches for dealing with data imbalance are ill-suited for image data: oversampling methods such as SMOTE and its derivatives lead to creation of unrealistic synthetic observations, whereas undersampling

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-03-31
Haotong Qin; Ruihao Gong; Xianglong Liu; Xiao Bai; Jingkuan Song; Nicu Sebe

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-03-30
Sergey Tarasenko; Fumihiko Takahashi

The CNNs have achieved a state-of-the-art performance in many applications. Recent studies illustrate that CNN's recognition accuracy drops drastically if images are noise corrupted. We focus on the problem of robust recognition accuracy of noise-corrupted images. We introduce a novel network architecture called Streaming Networks. Each stream is taking a certain intensity slice of the original image

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-03-30
Zachary Polizzi; Chuan-Yung Tsai

Generative adversarial networks (GANs) are capable of generating strikingly realistic samples but state-of-the-art GANs can be extremely computationally expensive to train. In this paper, we propose the fused propagation (FusedProp) algorithm which can be used to efficiently train the discriminator and the generator of common GANs simultaneously using only one forward and one backward propagation.

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-03-29
Ciro Javier Diaz Penedo

In this work we address the problem of predicting the model of a camera based on the content of their photographs. We use two set of features, one set consist in properties extracted from a Discrete Wavelet Domain (DWD) obtained by applying a 4 level Fast Wavelet Decomposition of the images, and a second set are Local Binary Patterns (LBP) features from the after filter noise of images. The algorithms

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-03-28
Andre G. Hochuli; Alceu S. Britto Jr.; Jean P. Barddal; Luiz E. S. Oliveira; Robert Sabourin

An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-03-28
Fenxi Xiao; Jie Zhang; Bo Huang; Xia Wu

This paper mainly discusses the generation of personalized fonts as the problem of image style transfer. The main purpose of this paper is to design a network framework that can extract and recombine the content and style of the characters. These attempts can be used to synthesize the entire set of fonts with only a small amount of characters. The paper combines various depth networks such as Convolutional

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-03-27
Fenxi Xiao; Bo Huang; Xia Wu

In this paper, we propose and end-to-end deep Chinese font generation system. This system can generate new style fonts by interpolation of latent style-related embeding variables that could achieve smooth transition between different style. Our method is simpler and more effective than other methods, which will help to improve the font design efficiency

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-06
Patrick Wspanialy; Justin Brooks; Medhat Moussa

We introduce a labeling tool and dataset aimed to facilitate computer vision research in agriculture. The annotation tool introduces novel methods for labeling with a variety of manual, semi-automatic, and fully-automatic tools. The dataset includes original images collected from commercial greenhouses, images from PlantVillage, and images from Google Images. Images were annotated with segmentations

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Ning Yu; Ke Li; Peng Zhou; Jitendra Malik; Larry Davis; Mario Fritz

Generative Adversarial Networks (GANs) have brought about rapid progress towards generating photorealistic images. Yet the equitable allocation of their modeling capacity among subgroups has received less attention, which could lead to potential biases against underrepresented minorities if left uncontrolled. In this work, we first formalize the problem of minority inclusion as one of data coverage

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-04
Chairi Kiourt; George Pavlidis; Stella Markantonatou

Automatic image-based food recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches enabled the identification of food types and their ingredients. The contents of food dishes are typically deformable objects, usually including complex semantics, which makes the task of defining their

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-04
Abrar Zahin; Le Thanh Tan; Rose Qingyang Hu

In this paper, we propose a novel framework for the smart healthcare system, where we employ the compressed sensing (CS) and the combination of the state-of-the-art machine learning based denoiser as well as the alternating direction of method of multipliers (ADMM) structure. This integration significantly simplifies the software implementation for the lowcomplexity encoder, thanks to the modular structure

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-04
Sandor Konya; Sai Natarajan T R; Hassan Allouch; Kais Abu Nahleh; Omneya Yakout Dogheim; Heinrich Boehm

The aim of this study is to investigate the segmentation accuracies of different segmentation networks trained on 730 manually annotated lateral lumbar spine X-rays. Instance segmentation networks were compared to semantic segmentation networks. The study cohort comprised diseased spines and postoperative images with metallic implants. The average mean accuracy and mean intersection over union (IoU)

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-04
David A. Noever; Sam E. Miller Noever

Despite rapid advances in image-based machine learning, the threat identification of a knife wielding attacker has not garnered substantial academic attention. This relative research gap appears less understandable given the high knife assault rate (>100,000 annually) and the increasing availability of public video surveillance to analyze and forensically document. We present three complementary methods

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-03
Victor L. F. Souza; Adriano L. I. Oliveira; Rafael M. O. Cruz; Robert Sabourin

High number of writers, small number of training samples per writer with high intra-class variability and heavily imbalanced class distributions are among the challenges and difficulties of the offline Handwritten Signature Verification (HSV) problem. A good alternative to tackle these issues is to use a writer-independent (WI) framework. In WI systems, a single model is trained to perform signature

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-07
Victor L. F. Souza; Adriano L. I. Oliveira; Rafael M. O. Cruz; Robert Sabourin

This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a context of Handwritten Signature Verification (HSV). SigNet is a state of the art Deep CNN model for feature representation in the HSV context and contains 2048 dimensions. Some of these dimensions may include redundant information in the dissimilarity representation

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-03
Re'em Harel; Matan Rusanovsky; Yehonatan Fridman; Assaf Shimony; Gal Oren

In fluid dynamics, one of the most important research fields is hydrodynamic instabilities and their evolution in different flow regimes. The investigation of said instabilities is concerned with the highly non-linear dynamics. Currently, three main methods are used for understanding of such phenomenon - namely analytical models, experiments and simulations - and all of them are primarily investigated

更新日期：2020-04-08
• arXiv.cs.CV Pub Date : 2020-04-03
Dario Sitnik; Ivica Kopriva

Insufficient capability of existing subspace clustering methods to handle data coming from nonlinear manifolds, data corruptions, and out-of-sample data hinders their applicability to address real-world clustering and classification problems. This paper proposes the robust formulation of the self-supervised convolutional subspace clustering network ($S^2$ConvSCN) that incorporates the fully connected

更新日期：2020-04-08
Contents have been reproduced by permission of the publishers.

down
wechat
bug