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Visual commonsense reasoning with directional visual connections Research Articles
Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000722
Keywords： 视觉常识推理；有向连接网络；视觉神经元连接；情景化连接；有向连接
Visual knowledge: an attempt to explore machine creativity Perspectives
Yueting Zhuang, Siliang Tang,yzhuang@zju.edu.cn,siliang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2100116
Keywords： 思维科学；形象思维推理；视觉知识表达；视觉场景图
A survey of script learning Review
Yi Han, Linbo Qiao, Jianming Zheng, Hefeng Wu, Dongsheng Li, Xiangke Liao,hanyi12@nudt.edu.cn,qiao.linbo@nudt.edu.cn,zhengjianming12@nudt.edu.cn,wuhefeng@mail.sysu.edu.cn,dsli@nudt.edu.cn,xkliao@nudt.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 3, Pages 287-436 doi: 10.1631/FITEE.2000347
Keywords： 脚本学习；自然语言处理；常识知识建模；事件推理
Miniaturized five fundamental issues about visual knowledge Perspectives
Yun-he Pan,panyh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2040000
Keywords： 视觉知识表达；视觉识别；视觉形象思维模拟；视觉知识学习；多重知识表达
Characters of topological relations and its applications in spatial reasoning
Li Chengming and Liu Xiaoli
Strategic Study of CAE 2013, Volume 15, Issue 5, Pages 14-19
In the paper,the formal representation of topological relationships are proposed according to our early research results, and the characters are also introduced. Based on these characters, the results for com-posite topological relations are introduced. At last, the applications in spatial reasoning are proposed.
Keywords： topological relationship composite of topological relations spatial reasoning algbra reasoning logical reasoning
Three-dimensional shape space learning for visual concept construction: challenges and research progress Perspective
Xin TONG
Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 9, Pages 1290-1297 doi: 10.1631/FITEE.2200318
Keywords： 视觉概念；视觉知识；三维几何学习；三维形状空间；三维结构
混合-增强智能：协作与认知 Review
南宁 郑,子熠 刘,鹏举 任,永强 马,仕韬 陈,思雨 余,建儒 薛,霸东 陈,飞跃 王
Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 153-179 doi: 10.1631/FITEE.1700053
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense Feature Article
Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu
Engineering 2020, Volume 6, Issue 3, Pages 310-345 doi: 10.1016/j.eng.2020.01.011
Recent progress in deep learning is essentially based on a “big data for small tasks” paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a “small data for big tasks” paradigm, wherein a single artificial intelligence (AI) system is challenged to develop “common sense,” enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of “why” and “how,” beyond the dominant “what” and “where” framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the “dark matter” of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace “dark” humanlike common sense for solving novel tasks.
Keywords： Computer vision Artificial intelligence Causality Intuitive physics Functionality Perceived intent Utility
A quantitative attribute-based benchmark methodology for single-target visual tracking Article
Wen-jing KANG, Chang LIU, Gong-liang LIU
Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3, Pages 405-421 doi: 10.1631/FITEE.1900245
Keywords： Visual tracking Performance evaluation Visual attributes Computer vision
Visual Inspection Technology and its Application
Ye Shenghua,Zhu Jigui,Wang Zhong,Yang Xueyou
Strategic Study of CAE 1999, Volume 1, Issue 1, Pages 49-52
Visual inspection, especially, the active visual inspection and passive visual inspection based on triangulation method has advantages of non-contact, rapid speed, flexibility, etc. Visual inspection is a advanced inspection technology, satisfies modern manufacturing demands. This paper discusses the principle of visual inspection, studies several developed applied visual inspection systems, these systems demostrate wide application foreground of visual inspection from different points of view.
Keywords： active visual inspection passive visual inspection inspection system modern manufacturing
Application of Uncertainty Reasoning Theory to Satellite Fault Detection and Diagnosis
Yang Tianshe,Li Huaizu,Cao Yuping
Strategic Study of CAE 2003, Volume 5, Issue 2, Pages 68-74
Generally, reasoning theory can be divided into certainty reasoning theory and uncertainty reasoning theory. Traditionally, certainty reasoning theory is used to detect and diagnose satellite faults. However, in practice, it is difficult to detect and diagnose some satellite faults automatically only by use of certainty reasoning theory. The reason is that detection and diagnosis of these faults requires reasonable reasoning and fault-tolerant capability, but certainty reasoning theory can not realize the capability. Fortunately, uncertainty reasoning theory can meet this requirement. Now, it is attracting attention of many researchers and practitioners in the space field all over the world that uncertainty reasoning theory is applied to detect and diagnose the satellite faults which can not be handled properly by certainty reasoning theory. Uncertainty reasoning theory includes several kinds of theories, such as inclusion degree theory, rough set theory, evidence reasoning theory, probabilistic reasoning theory, fuzzy reasoning theory, and so on. This paper introduces three new methods to detect and diagnose the satellite faults, in which inclusion degree theory, rough set theory and evidence reasoning theory of the uncertainty reasoning theory are used respectively.
Keywords： satellite fault detection diagnosis uncertainty reasoning theory
Causal Inference Review
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang
Engineering 2020, Volume 6, Issue 3, Pages 253-263 doi: 10.1016/j.eng.2019.08.016
Causal inference is a powerful modeling tool for explanatory analysis, which might enable current machine learning to become explainable. How to marry causal inference with machine learning to develop eXplainable Artificial Intelligence (XAI) algorithms is one of key steps towards to the artificial intelligence 2.0. With the aim of bringing knowledge of causal inference to scholars of machine learning
and artificial intelligence, we invited researchers working on causal inference to write this survey from different aspects of causal inference. This survey includes the following sections: "Estimating average treatment effect: A brief review and beyond" from Dr. Kun Kuang, "Attribution problems in counterfactual inference" from Prof. Lian Li, "The Yule-Simpson paradox and the surrogate paradox" from Prof. Zhi Geng, "Causal potential theory" from Prof. Lei Xu, "Discovering causal information from observational data" from Prof. Kun Zhang, "Formal argumentation in causal reasoning and explanation" from Profs. Beishui Liao and Huaxin Huang, "Causal inference with complex experiments" from Prof. Peng Ding, "Instrumental variables and negative controls for observational studies" from Prof. Wang Miao, and "Causal inference with interference" from Dr. Zhichao Jiang.
Keywords： Causal inference Instructive variables Negative control Causal reasoning and explanation Causal discovery Counter factual inference Treatment effect estimation
Progress in Neural NLP: Modeling, Learning, and Reasoning Review
Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum
Engineering 2020, Volume 6, Issue 3, Pages 275-290 doi: 10.1016/j.eng.2019.12.014
Natural language processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling computers to understand and process human languages. In the last five years, we have witnessed the rapid development of NLP in tasks such as machine translation, question-answering, and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data. In this paper, we will review the latest progress in the neural network-based NLP framework (neural NLP) from three perspectives: modeling, learning, and reasoning. In the modeling section, we will describe several fundamental neural network-based modeling paradigms, such as word embedding, sentence embedding, and sequence-to-sequence modeling, which are widely used in modern NLP engines. In the learning section, we will introduce widely used learning methods for NLP models, including supervised, semi-supervised, and unsupervised learning; multitask learning; transfer learning; and active learning. We view reasoning as a new and exciting direction for neural NLP, but it has yet to be well addressed. In the reasoning section, we will review reasoning mechanisms, including the knowledge, existing non-neural inference methods, and new neural inference methods. We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledge-driven neural NLP models to handle complex tasks. At the end of this paper, we will briefly outline our thoughts on the future directions of neural NLP.
Keywords： Natural language processing Deep learning Modeling learning and Reasoning
Mathematical Reasoning Challenges Artificial Intelligence
Sean O’Neill
Engineering 2019, Volume 5, Issue 5, Pages 817-818 doi: 10.1016/j.eng.2019.08.009
An ANFIS-based Approach for Predicting MiningInduced Surface Subsidence
Ding Dexin,Zhang Zhijun,Bi Zhongwei
Strategic Study of CAE 2007, Volume 9, Issue 1, Pages 33-39
Current approaches for predicting mining induced surface subsidence have a drawback in common that they predict the subsidence only on the basis of a physical or mechanical approach irrespective of the practical examples in engineering practice in mining induced surface subsidence.However,these experiences created in engineering practice are of great value and full use should be made of them to establish an approach for predicting mining induced surface subsidence.Therefore,this paper accumulated a lot of practical examples of mining induced surface subsidence,integrated these examples by using adaptive neuro-fuzzy inference system (ANFIS)and established an ANFIS-based approach for predicting mining induced surface subsidence.The approach was further tested by using practical examples of mining induced surface subsidence.The results show that the approach can converge quickly,fit the data in very good agreement and make generalization prediction with high accuracy.
Keywords： underground mining mining induced surface subsidence adaptive neuro唱fuzzy inferencesystem
Title Author Date Type Operation
Visual commonsense reasoning with directional visual connections
Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn
Journal Article
Visual knowledge: an attempt to explore machine creativity
Yueting Zhuang, Siliang Tang,yzhuang@zju.edu.cn,siliang@zju.edu.cn
Journal Article
A survey of script learning
Yi Han, Linbo Qiao, Jianming Zheng, Hefeng Wu, Dongsheng Li, Xiangke Liao,hanyi12@nudt.edu.cn,qiao.linbo@nudt.edu.cn,zhengjianming12@nudt.edu.cn,wuhefeng@mail.sysu.edu.cn,dsli@nudt.edu.cn,xkliao@nudt.edu.cn
Journal Article
Miniaturized five fundamental issues about visual knowledge
Yun-he Pan,panyh@zju.edu.cn
Journal Article
Characters of topological relations and its applications in spatial reasoning
Li Chengming and Liu Xiaoli
Journal Article
Three-dimensional shape space learning for visual concept construction: challenges and research progress
Xin TONG
Journal Article
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu
Journal Article
A quantitative attribute-based benchmark methodology for single-target visual tracking
Wen-jing KANG, Chang LIU, Gong-liang LIU
Journal Article
Visual Inspection Technology and its Application
Ye Shenghua,Zhu Jigui,Wang Zhong,Yang Xueyou
Journal Article
Application of Uncertainty Reasoning Theory to Satellite Fault Detection and Diagnosis
Yang Tianshe,Li Huaizu,Cao Yuping
Journal Article
Causal Inference
Kun Kuang, Lian Li, Zhi Geng, Lei Xu, Kun Zhang, Beishui Liao, Huaxin Huang, Peng Ding, Wang Miao, Zhichao Jiang
Journal Article
Progress in Neural NLP: Modeling, Learning, and Reasoning
Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum
Journal Article
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