Workshop 1 (Challenge)
竞赛主题：MOre than Common Object Detection (MOCOD) – Occlusion and Small Objects
Huimin Ma, Tsinghua University
Yunzhi Xue, Institute of Software, Chinese Academy of Sciences
Qi Guo, Institute of Computing Technology, Chinese Academy of Sciences
Jianguo Cao, University of Science and Technology Beijing
Workshop 2 (Challenge)
我们提供了一组训练集和验证集，该训练集和验证集来自“Benchmarking Single Image Dehazing and Beyond”。训练集和验证集共包括5500张合成的雾图、清晰的图像以及深度信息。有一个由20张真实世界的轨道交通监控雾霾图像组成的测试集，我们将会稍后发布。
- 4月20日 发布竞赛方案
- 6月10日 报名截止
- 8月01日 发布测试集
- 8月05日 提交结果截至
- 8月12日 公布竞赛结果
- 8月15日 获胜者提交代码
- 8月23-25 ICIG 2019大会期间 竞赛颁奖
竞赛主席：秦勇 教授，贾利民 教授 倪蓉蓉 教授
- Workshop Title:
Automated AI in Vision—Opportunities and Challenges
- Topics Covered:
–Auto Machine Learning in Vision
–Auto Acceleration for Vision Applications
–Vision in Edge Applications
–Data Privacy Protection
This workshop aims to explore the opportunities and challenges in automated AI, especially for vision related applications. Automated AI refers to make the AI process automatically with high accuracy and efficiency. It covers each stages of AI process, including data preparation, labeling, training, deployment and prediction. The topic is not only for AI algorithm aspects, but also for AI system. In this workshop, we want to focus the topics on four points: AutoML in vision, training and inference acceleration, vision in edge applications and data privacy protection. For AutoML in vision, we will discuss how to enhance the transferability of models through auto machine learning, and how to optimize neural network architectures and hyper-parameters w/wo domain knowledge. For visual applications, model performance and system performance are both important to a successful application. Taking object detection as an example, the inference speed is also an important evaluation angle for the model or algorithm evaluation. For a real application, we should consider the trade off between processing speed and model accuracy. How to select the corresponding meta architectures and algorithms adaptively based on the hardware status, such as memory, bandwidth, etc. automatically? How to ensure the required performance during training and inference stage? We want to investigate more through this workshop. And Vision in edge is also our focus. In this workshop, we will discuss the challenges and progress for vision in edge related applications, such as vision with internet of things (IoT) edge, vision in mobile applications, etc. With the fast development of 5G technology, we believe AI in edge and mobile will be more attractive.
Title: Automated AI—Effectiveness, Efficiency and Trusted
Abstract:Automated AI means using automated tools and methods to make the learning and inference processing automation. It includes not only AI for data analysis, but also data analysis and pre-processing for better AI models. In this topic, we will give an overview about automated AI, especially from the effectiveness, efficiency and trusted aspects to introduce techniques involved.
Biography: Dr. Yong Qin is the Senior Technology Staff Member of Cognitive Healthcare and Cognitive Interaction in IBM Research – China. In 1996, Dr. Qin joined in IBM Research – China after earning his Ph.D degree in speech signal processing field from Institute of Acoustic (IOA) of Chinese Academy of Sciences. During the past years, his work widely covered research and development activities of IBM speech products and human language technologies, including the first Mandarin ViaVoice Dictation System, IBM ViaVoice Telephony Runtime & Tools, IBM Embedded ViaVoice, IBM Websphere Voice Server, IBM Speech Transcription Technology (GALE) and IBM Real Time Translation Service (RTTS), text visualization, psycholinguistic, etc. Meanwhile, Dr. Qin also led cognitive Healthcare team, drawing on over a decade of healthcare-specific data and analytics expertise, demonstrated a cognitive healthcare advisor, which assists primary care physicians to make evidence-based decision, leading to better diagnoses, treatment, and management of chronic diseases. Recently, Dr. Qin mainly focus on research strategy management and lab operation of IBM Research China. Dr. Qin is one of the vice-presidents of National Man-Machine Speech Communication committee. Dr. Qin used to be a part-time professor of Nankai University.
Title:Transferability of AutoML in Vision
Abstract:In this topic, Chao will give a brief introduction of considerable literature on AutoML which are based on genetic algorithms, random search, Bayesian optimization, reinforcement learning and continuous differentiable method. Then, considering most existing AutoML approaches required considerable overhead for model searching, he will present a so called transferable AutoML approach that leverages previously trained models to speed up the search process for new tasks and datasets.
Biography: Xue Chao is a research scientist of IBM Research China. He mainly focuses on the optimization of machine learning/deep learning applications from hyper-parameter tuning to neural network search. He leads the project of performance optimization and tools for big data and deep learning on platform, which becomes a key component of IBM Watson Machine Learning Accelerator. Meanwhile, his research paper about AutoML–Transferable AutoML by Model Sharing over Grouped Datasets is accepted and published by the top conference of Computer Vision, CVPR. Also, at the top conference of Computer Science, PACT (The International Conference on Parallel Architecture and Compilation Techniques) 2016, he presented their accepted paper about big-data and machine learning at Haifa, Israel. He has ever taught AI courses at University of Chinese Academy of Sciences and Beijing Normal University, etc., and has finished more than 20 patents.
Title:Vision on Edge—Challenges and Progress
Abstract:With more advanced deep neural networks developed every day, there is a big challenge to deploy such complicated models in real-time system under strict constraints on edge devices with power budget, delay, form factor and cost. Researchers are actively investigating approaches to map the various deep learning workloads onto different kinds of accelerators by taking advantage of their improved latency and energy efﬁciency in order to deliver intelligence to edge devices. This is the golden age of architecture innovations for AI accelerators. In this talk, I will briefly introduce our innovations on AI accelerator architectures and model compression work in past several years.
Biography: Jun Song Wang is the Research Staff Member in IBM Research, China. He has worked on wireless communications, signal processing and deep learning acceleration research in IBM for more than 9 years. He was one of the major contributors of the long-distance wireless communication platform. Currently, he is working on the deep learning acceleration in cloud and edge devices, especially for the FPGA acceleration. He has more than 20 patents granted worldwide and 10+ publications in top conferences and journals. He has received the Best Paper Award in ICCAD’18.
Title:Privacy Preserving in Medical Image Processing
Abstract:Recently, deep convolutional neural networks (CNNs) have achieved great success in pathological image classification. However, the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset. That is privacy leakage. The smaller the dataset, the worse the privacy leakage. In this topic, we will give an overview about privacy leakage, and introduce a novel stochastic gradient descent (SGD) scheme, named patient privacy preserving SGD (P3SGD), to protect privacy and regularize the CNN model. We find that the models trained with P3SGD are resistant to the model-inversion attack compared with those trained using non-private SGD.
Biography: Shiwan Zhao is a Research Staff Member at IBM Research – China. He received B.S. and M.S. degrees in computer science from Tsinghua University in 1998 and 2000, respectively. Mr. Zhao subsequently joined the IBM Research – China, where he has been working on pervasive computing, recommender systems, cognitive healthcare, computer vision, and NLP. His work has appeared in top conferences/journals, like IUI/RecSys/SDM/KDD/SIGIR/TIST/IJCAI/AAAI/CVPR, and he is the PC member of SIGIR and CIKM. He is the IBM Master Inventor with more than 90 patents filed.
Title:Customized Learning for Video Analysis
AbstractCustomized learning is very important for enterprise applications. PowerAI Vision is a visualized AI customized learning platform for computing vision. PowerAI Vision aims at automating AI design without coding or deep learning expertise. It provides full customized AI learning cycle from dataset annotation to model deployment for inference. In this topic, we will introduce how we balance AI customization and automation on PowerAI Vision, and how we achieve high accuracy and high performance inference on elastic computing resources.
Biography: Dr. Yubo Li is from IBM Research China, Beijing. He has over 5 years of experience on AI platform design and AI workload optimization. He is the architect of PowerAI Vision, an IBM product of AI learning platform. He is also leading GPU enablement and optimization for AI container cloud in IBM. His interests include AI platform architecture design, deep learning acceleration, etc.
Workshop chairs 联系方式：
- Si Liu(firstname.lastname@example.org)，北京航空航天大学
- Jiashi Feng(email@example.com)，新加坡国立大学