scholarly journals Leveraging Multi-View Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation

2020 ◽  
Vol 34 (07) ◽  
pp. 12685-12692
Author(s):  
Renjiao Yi ◽  
Ping Tan ◽  
Stephen Lin
Keyword(s):  
State Of The Art ◽  
Image Decomposition ◽  
Local Color ◽  
Guidance Cue ◽  
Object Appearance ◽  
Unsupervised Training ◽  
Unsupervised Approach ◽  
Object Level ◽  
Yield State ◽  
Do So

We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images. To do so, we construct a multi-view dataset by collecting numerous customer product photos online, which exhibit large illumination variations that make them suitable for training of reflectance separation and can facilitate object-level decomposition. The main contribution of our approach is a proposed image representation based on local color distributions that allows training to be insensitive to the local misalignments of multi-view images. In addition, we present a new guidance cue for unsupervised training that exploits synergy between highlight separation and intrinsic image decomposition. Over a broad range of objects, our technique is shown to yield state-of-the-art results for both of these tasks.

scholarly journals ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

2021 ◽  
Author(s):  
Anil S. Baslamisli ◽  
Partha Das ◽  
Hoang-An Le ◽  
Sezer Karaoglu ◽  
Theo Gevers
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
State Of The Art ◽  
Image Decomposition ◽  
Natural Environments ◽  
Decomposition Algorithms ◽  
Ambient Light ◽  
Fine Grained ◽  
Large Scale Dataset ◽  
Direct Illumination

AbstractIn general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.

scholarly journals Digital entrepreneurship in Morocco: measurement model validation

2021 ◽  
Vol 119 ◽  
pp. 03004
Author(s):  
Zakia Saoura ◽  
Ahmed Abriane ◽  
Aniss Moumen
Keyword(s):  
Quantitative Research ◽  
State Of The Art ◽  
Measurement Model ◽  
Arab Countries ◽  
Three Dimensions ◽  
Measurement Scale ◽  
Global Entrepreneurship Monitor ◽  
Entrepreneurial Motivations ◽  
Digital Entrepreneurship ◽  
Do So

According to the 2017 Global Entrepreneurship Monitor report, there are 6.5 million adults aged 18-64 planning to start an entrepreneurial career by 2020. However, the gap between attempt and effective creations remains one of the largest within Arab countries (40% versus 9%). Given these statistics, we ask the question about the profile of the Moroccan entrepreneur. In this paper, we opted for a quantitative research methodology on an exploratory sample. We distributed a questionnaire to a sample of eighty Moroccan entrepreneurs representing different regions of Morocco. The objective of our study is to validate a measurement scale of three dimensions: 1/ entrepreneurial motivations, 2/ skills, and 3/ behaviour in the Moroccan context. To do so, we present, in the first part, a literature review on digital entrepreneurship. Then, we establish a state of the art of entrepreneurship in Morocco. Then, we show our methodology. Finally, we reveal and discuss the results of our study.

scholarly journals On international migration: A review essay

2017 ◽  
Vol 44 (3-4) ◽  
pp. 200
Author(s):  
Anthony C. Masi
Keyword(s):  
Social Policy ◽  
International Migration ◽  
Review Essay ◽  
State Of The Art ◽  
Immigrant Integration ◽  
Empirical Information ◽  
New Information ◽  
Four Books ◽  
Unanticipated Consequences ◽  
Do So

International migration continues to reshape our world, sometimes in predictable ways, but often with unanticipated consequences. The four books reviewed here provide new information and important insights regarding migration and migrant adjustment. They do so either by dealing with the policy dimension of this vast topic (Freeman and Mirilovic; Phillimore) or by delving deeply into the issue of immigrant integration (Scholten et al.; Waters and Gerstein Pineau). These editors took four different approaches to their task: (1) a compilation of already published works on the topic (Phillimore); (2) original pieces on topics or countries but following a predetermined framework (Scholten, et al.); (3) chapters designed to test theories against available empirical information (Freeman and Mirilovic); and (4) a comprehensive group-written “state of the art” for a single country (Waters and Gerstein Pineau). Together, the books provide an impressive array of scholarship from a variety of disciplinary perspectives on the links between migration and social policy and on immigrant integration.

Women in Brazilian CS Research Community

2012 ◽  
pp. 1824-1839
Author(s):  
Mirella M. Moro ◽  
Taisy Weber ◽  
Carla M.D.S. Freitas
Keyword(s):  
Computer Science ◽  
State Of The Art ◽  
Research Community ◽  
Current Situation ◽  
The State ◽  
Do So

Many communities have been concerned with the problem of bringing more girls to technology and science related areas. The authors believe that the first step in order to solve such a problem is to understand the current situation, like to investigate the “state-of-the-art” of the problem. Therefore, in this chapter, they present the first study to identify which areas of Computer Science have more and less feminine participation. In order to do so, they have considered the program committees of the Brazilian conferences in those areas. The authors’ study evaluates the 2008 and previous editions of such conferences. They also discuss some Brazilian initiatives to bring more girls to Computer Science as well present what else can be done.

On the Privacy and Utility of Anonymized Social Networks

2013 ◽  
Vol 4 (2) ◽  
pp. 1-34 ◽  
Author(s):  
Yi Song ◽  
Xuesong Lu ◽  
Sadegh Nobari ◽  
Stéphane Bressan ◽  
Panagiotis Karras
Keyword(s):  
Social Networks ◽  
Social Media ◽  
State Of The Art ◽  
Social Groups ◽  
Trade Off ◽  
Private Lives ◽  
The Social ◽  
Before And After ◽  
Do So

One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.

scholarly journals EHSOD: CAM-Guided End-to-End Hybrid-Supervised Object Detection with Cascade Refinement

2020 ◽  
Vol 34 (07) ◽  
pp. 10778-10785
Author(s):  
Linpu Fang ◽  
Hang Xu ◽  
Zhili Liu ◽  
Sarah Parisot ◽  
Zhenguo Li
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Detection System ◽  
Parameter Tuning ◽  
Heat Map ◽  
Level Data ◽  
End To End ◽  
Weakly Supervised ◽  
Yield State ◽  
Activation Heat

Object detectors trained on fully-annotated data currently yield state of the art performance but require expensive manual annotations. On the other hand, weakly-supervised detectors have much lower performance and cannot be used reliably in a realistic setting. In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fully-annotated data and fully exploiting cheap data with image-level labels. State of the art methods typically propose an iterative approach, alternating between generating pseudo-labels and updating a detector. This paradigm requires careful manual hyper-parameter tuning for mining good pseudo labels at each round and is quite time-consuming. To address these issues, we present EHSOD, an end-to-end hybrid-supervised object detection system which can be trained in one shot on both fully and weakly-annotated data. Specifically, based on a two-stage detector, we proposed two modules to fully utilize the information from both kinds of labels: 1) CAM-RPN module aims at finding foreground proposals guided by a class activation heat-map; 2) hybrid-supervised cascade module further refines the bounding-box position and classification with the help of an auxiliary head compatible with image-level data. Extensive experiments demonstrate the effectiveness of the proposed method and it achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data, e.g. 37.5% mAP on COCO. We will release the code and the trained models.

The rationale for extended lactations in dairy cows

1996 ◽  
Vol 1996 ◽  
pp. 1-1
Author(s):  
C H Knight
Keyword(s):  
Dairy Cows ◽  
Milk Production ◽  
Milk Yield ◽  
State Of The Art ◽  
Simple Calculation ◽  
Calving Interval ◽  
Daily Production ◽  
Good Management ◽  
Do So ◽  
Intensive System

State of the art milk production encourages an intensive system of maximising peak milk yield and minimising calving interval, epitomized by the 40 kg peak daily production and 10,000 kg 305 d lactation yield of a well bred, well fed Holstein. Given good management, milk yield typically declines at approximately 2% per week. It requires only a simple calculation to show, therefore, that this same cow would still be yielding around 20 kg of milk daily at 2 months before calving. The dilemma for the farmer is whether to dry her off or not, and if he is sensible he will probably do so. However, if he is really clever he will then change his rebreeding policy!

scholarly journals Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing

2019 ◽  
Vol 33 ◽  
pp. 3598-3605 ◽  
Author(s):  
Ryosuke Furuta ◽  
Naoto Inoue ◽  
Toshihiko Yamasaki
Keyword(s):  
Image Processing ◽  
Reinforcement Learning ◽  
State Of The Art ◽  
Great Success ◽  
Local Color ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Effective Learning ◽  
Pixel Value ◽  
Problem Setting

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied.We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning.

Telephone Survey Methods: The State of the Art

1979 ◽  
Vol 43 (3) ◽  
pp. 68-78 ◽  
Author(s):  
Tyzoon T. Tyebjee
Keyword(s):  
Survey Research ◽  
Consumer Research ◽  
Telephone Survey ◽  
State Of The Art ◽  
Survey Methods ◽  
Research Data ◽  
The State ◽  
Do So

Telephone interviewing is currently the dominant method of survey research. Managers who rely on consumer research data collected by this method should do so with an appreciation of its advantages and limitations.

scholarly journals Unsupervised Action Proposals Using Support Vector Classifiers for Online Video Processing

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2953
Author(s):  
Marcos Baptista Ríos ◽  
Roberto Javier López-Sastre ◽  
Francisco Javier Acevedo-Rodríguez ◽  
Pilar Martín-Martín ◽  
Saturnino Maldonado-Bascón
Keyword(s):  
Video Processing ◽  
State Of The Art ◽  
Learning To Rank ◽  
Support Vector ◽  
New Approach ◽  
Support Vector Classifier ◽  
Current State ◽  
Video Frames ◽  
Unsupervised Approach ◽  
Video Sensor

In this work, we introduce an intelligent video sensor for the problem of Action Proposals (AP). AP consists of localizing temporal segments in untrimmed videos that are likely to contain actions. Solving this problem can accelerate several video action understanding tasks, such as detection, retrieval, or indexing. All previous AP approaches are supervised and offline, i.e., they need both the temporal annotations of the datasets during training and access to the whole video to effectively cast the proposals. We propose here a new approach which, unlike the rest of the state-of-the-art models, is unsupervised. This implies that we do not allow it to see any labeled data during learning nor to work with any pre-trained feature on the used dataset. Moreover, our approach also operates in an online manner, which can be beneficial for many real-world applications where the video has to be processed as soon as it arrives at the sensor, e.g., robotics or video monitoring. The core of our method is based on a Support Vector Classifier (SVC) module which produces candidate segments for AP by distinguishing between sets of contiguous video frames. We further propose a mechanism to refine and filter those candidate segments. This filter optimizes a learning-to-rank formulation over the dynamics of the segments. An extensive experimental evaluation is conducted on Thumos’14 and ActivityNet datasets, and, to the best of our knowledge, this work supposes the first unsupervised approach on these main AP benchmarks. Finally, we also provide a thorough comparison to the current state-of-the-art supervised AP approaches. We achieve 41% and 59% of the performance of the best-supervised model on ActivityNet and Thumos’14, respectively, confirming our unsupervised solution as a correct option to tackle the AP problem. The code to reproduce all our results will be publicly released upon acceptance of the paper.

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