【计算机类】各领域截稿信息5条

全文截稿: 2018-01-05

开会时间: 2018-04-23

会议难度: ★★

CCF分类: 无

会议地点: Singapore

网址：http://www.icmva.org/

The International Conference on Machine Vision and Applications (ICMVA 2018) will be held in Singapore during April 23-25, 2018.

The field of machine vision and application, has been growing at a fast pace. As in most fast-developing fields, not all aspects of machine vision that are of interest to active researchers are useful to the designers and users of a vision system for a specific application. This conference is intended to provide a balanced introduction to machine vision. Basic concepts and application are introduced and discussed with various essential mathematical elements etc onsite. The details to allow implementation and use of vision algorithm in practical application are provided, and engineering aspects of techniques are emphasized. The problems to apply machine vision are going to be solved.

What's more, the aim of this conference is to bring together researchers and practitioners from both academia and industry, and to stimulate the exchange of knowledge through intensive discussions on the cutting-edge research topics listed below: Vision Sensing, Machine Vision Applications, Factory Automation and Robotics, Intelligent Transport Systems, Surveillance, Human Computer Interaction, Biomedical, Multimedia, and Life Science and Engineering, etc.

We hope that the conference results constituted significant contribution to the knowledge in these up to date scientific field. The organizing committee of conference is pleased to invite prospective authors to submit their original manuscripts to ICMVA 2018.

全文截稿: 2018-01-05

影响因子: 1.933

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：硬件 - 3区

• 小类 : 计算机：软件工程 - 3区

网址: https://www.computer.org/micro/

Approximate computing covers a broad spectrum of systems and architectures where quality (or accuracy) serves as a design parameter, enabling trade-offs between quality of results and efficiency. This computing paradigm has garnered much research activity in recent years for two reasons. First, the dark future of CMOS scaling has forced architects to come up with new ways to squeeze every last ounce of efficiency in their designs. Second, there has been growing interest in applications that are inherently probabilistic, imprecise, or noisy (such as machine learning, multimedia, and sensor devices).

Approximate computing introduces fundamentally new research avenues due to its unique design principles: 1) unlike when tuning for efficiency, the quality knob has strict constraints in order to maintain correctness in the system; and 2) users and programmers need to have more active roles in deciding when quality is acceptable. This special issue of IEEE Micro will explore exciting, new ideas in the vast design space of approximate computing.

Topics of interest include (but are not limited to):

- Architectural support for approximate computing
- Approximation techniques on emerging processor and memory technologies
- Design methodologies and tools for approximate hardware
- Analog and circuit-level approximation techniques
- Language, compiler, and operating system support for approximate architectures
- Hardware accelerators for approximation-tolerant application domains
- Techniques for monitoring and controlling approximation quality

全文截稿: 2018-01-08

开会时间: 2018-04-23

会议难度: ★★

CCF分类: 无

会议地点: Singapore

网址：http://www.icfcc.org/

Science and Engineering Institute (SCIEI) sincerely invite you to take part in 2018 The 10th International Conference on Future Computer and Communication (ICFCC 2018) in Singapore during April 23-25, 2018.

ICFCC is a leading annual conference of Future Computer and Communication for all researchers home and abroad. This conference focus on Future Computer and Communication. ICFCC conference will provide a valuable opportunity for researchers ,scholars and scientists to exchange their ideas face to face. We have the strong organization team, dependable reputation and wide sponsors all around the world. It will bring you an unexpected harvest. We welcome you to be a member of our big family.

全文截稿: 2018-01-15

影响因子: 8.222

CCF分类: A类

中科院JCR分区:

• 大类 : 工程技术 - 2区

• 小类 : 计算机：人工智能 - 2区

网址: http://www.springer.com/journal/11263/about

Deep learning is one of the most important breakthroughs in the field of artificial intelligence over the last decade. It has achieved great success in speech recognition, natural language processing, computer vision, and multimedia. Many face analysis tasks, including face detection, alignment, reconstruction, and recognition, benefit from the powerful representation learning capability of deep learning techniques. Not only there has been a constantly growing flow of related research papers, but also substantial progress has been achieved in real-world applications such as security, video surveillance, and human-computer interaction.

While substantial progress has been achieved in face analysis with deep learning, many issues still remain and new problems emerge. For instance, the scalability of deep networks to large-scale unconstrained recognition needs be improved. In-the-wild facial attributes recognition with imbalance class distribution is still challenging. The accuracy and efficiency of detecting faces with a wide range of scales in a crowded scene still see a large room for improvement.

This special issue presents a great platform to make a definitive statement about the state of the art by providing a significant collective contribution to this emerging field of study. Specifically, we aim to solicit original contributions that: (1) present state-of-the-art theories related to deep learning for face analysis; (2) develop novel methods and applications; (3) survey the recent progress in this area; and (4) establish benchmark datasets.

Topics of Interest The list of possible topics includes, but is not limited to:

• Theory
o Deep learning
o Cross-domain feature learning and fusion
o Transfer learning
o Multitask learning
o Generative adversarial learning
o Multi-instance learning
o Weakly supervised learning
o Reinforcement learning
o Zero-shot / One-shot learning

• Applications
o Face detection
o Face alignment and tracking
o Face recognition
o Face verification
o Face clustering
o Face attribute recognition (including age and gender)
o Facial expression recognition
o Face hallucination and completion
o 3D face reconstruction
o Face parsing
o Face sketch synthesis and recognition

全文截稿: 2018-01-28

开会时间: 2018-05-09

会议难度: ★★

CCF分类: 无

会议地点: Toronto, Canada

网址：http://www.computerrobotvision.org/

CRV is an annual conference hosted in Canada, and co-located with Graphics Interface (GI) and Artificial Intelligence (AI).

A single registration covers attendance in all three conferences.

The CRV proceedings are published through the Conference Publishing Services (CPS).

Accepted papers will be submitted to Xplore. Xplore has published CRV accepted papers since 2004.

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