检索结果-南通市图书馆

causal reasoning in typical computer vision tasks

Science China(Technological Sciences) 2024年第1期67卷 105-120页

作者： ZHANG KeXuan SUN QiYu ZHAO ChaoQiang TANG Yang Key Laboratory of Advanced Control and Optimization for Chemical Process Ministry of EducationEast China University of Science and TechnologyShanghai 200237China National Key Laboratory of Air based Information Perception and Fusion Luoyang 471000China Luoyang Institute of Electro Optical Equipment of Avic Luoyang 471000China

Deep learning has revolutionized the field of artificial intelligence.Based on the statistical correlations uncovered by deep learning-based methods,computer vision tasks,such as autonomous driving and robotics,are growing rapidly.Despite being the basis of deep learning,such correlation strongly depends on the distribution of the original data and is susceptible to uncontrolled factors.Without the guidance of prior knowledge,statistical correlations alone cannot correctly reflect the essential causal relations and may even introduce spurious correlations.As a result,researchers are now trying to enhance deep learningbased methods with causal theory.causal theory can model the intrinsic causal structure unaffected by data bias and effectively avoids spurious correlations.This paper aims to comprehensively review the existing causal methods in typical vision and visionlanguage tasks such as semantic segmentation,object detection,and image captioning.The advantages of causality and the approaches for building causal paradigms will be summarized.Future roadmaps are also proposed,including facilitating the development of causal theory and its application in other complex scenarios and systems.

关键词： causal reasoning computer vision tasks vision-language tasks semantic segmentation object detection

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

causal reasoning Meets Visual Representation Learning: A Prospective Study

引用

Machine Intelligence Research 2022年第6期19卷 485-511页

作者： Yang Liu Yu-Shen Wei Hong Yan Guan-Bin Li Liang Lin School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China

Visual representation learning is ubiquitous in various real-world applications,including visual comprehension,video understanding,multi-modal analysis,human-computer interaction,and urban computing.Due to the emergence of huge amounts of multimodal heterogeneous spatial/temporal/spatial-temporal data in the big data era,the lack of interpretability,robustness,and out-of-distribution generalization are becoming the challenges of the existing visual models.The majority of the existing methods tend to fit the original data/variable distributions and ignore the essential causal relations behind the multi-modal knowledge,which lacks unified guidance and analysis about why modern visual representation learning methods easily collapse into data bias and have limited generalization and cognitive abilities.Inspired by the strong inference ability of human-level agents,recent years have therefore witnessed great effort in developing causal reasoning paradigms to realize robust representation and model learning with good cognitive ability.In this paper,we conduct a comprehensive review of existing causal reasoning methods for visual representation learning,covering fundamental theories,models,and datasets.The limitations of current methods and datasets are also discussed.Moreover,we propose some prospective challenges,opportunities,and future research directions for benchmarking causal reasoning algorithms in visual representation learning.This paper aims to provide a comprehensive overview of this emerging field,attract attention,encourage discussions,bring to the forefront the urgency of developing novel causal reasoning methods,publicly available benchmarks,and consensus-building standards for reliable visual representation learning and related real-world applications more efficiently.

关键词： causal reasoning visual representation learning reliable artificial intelligence spatial-temporal data multi-modal analysis

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Local-to-Global causal reasoning for Cross-Document Relation Extraction

引用

IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1608-1621页

作者： Haoran Wu Xiuyi Chen Zefa Hu Jing Shi Shuang Xu Bo Xu Institute of Automation Chinese Academy of SciencesBeijing 100190China. School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

Cross-document relation extraction(RE),as an extension of information extraction,requires integrating information from multiple documents retrieved from open domains with a large number of irrelevant or confusing noisy texts.Previous studies focus on the attention mechanism to construct the connection between different text features through semantic similarity.However,similarity-based methods cannot distinguish valid information from highly similar retrieved documents well.How to design an effective algorithm to implement aggregated reasoning in confusing information with similar features still remains an open issue.To address this problem,we design a novel local-toglobal causal reasoning(LGCR)network for cross-document RE,which enables efficient distinguishing,filtering and global reasoning on complex information from a causal perspective.Specifically,we propose a local causal estimation algorithm to estimate the causal effect,which is the first trial to use the causal reasoning independent of feature similarity to distinguish between confusing and valid information in cross-document RE.Furthermore,based on the causal effect,we propose a causality guided global reasoning algorithm to filter the confusing information and achieve global reasoning.Experimental results under the closed and the open settings of the large-scale dataset Cod RED demonstrate our LGCR network significantly outperforms the state-ofthe-art methods and validate the effectiveness of causal reasoning in confusing information processing.

关键词： causal reasoning cross document graph reasoning relation extraction(RE)

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Academic Freedom,Feminism and the Probabilistic Conception of Evidence

引用

Journal of Philosophy Study 2022年第6期12卷 322-328页

作者： Tom Vinci Department of Philosophy Dalhousie UniversityHalifaxNova ScotiaCanada

There is a current debate about the extent to which Academic Freedom should be permitted in our universities.On the one hand,we have traditionalists who maintain that Academic Freedom should be unrestricted:people who have the appropriate qualifications and accomplishments should be allowed to develop theories about how the world is,or ought to be,as they see fit.On the other hand,we have post-traditional philosophers who argue against this degree of Academic Freedom.I consider a conservative version of post-traditional philosophy that permits restrictions on Academic Freedom only if the following conditions are met,Condition 1:The dissemination of the results of a given research project R must cause significant harm to some people,especially to people from oppressed groups.Condition 2:Condition 1 must possess strong empirical support,and which accepts the following assumptions:(1)there is a world of objective facts that is,in principle,discoverable,(2)rational means are the means of discovering it and,(3)rational means requires strong empirical support.I define strong empirical support for an hypothesis h on evidence e in probabilistic terms,as a ratio of posterior to prior probabilities substantially exceeding 1.I now argue in favour of a research policy that accepts unrestricted Academic Freedom.My argument is that there is a formal and general quandary that arises within the standard theory of probability when we apply this account of empirical support to a set of possible causal hypotheses framed in such a way that the“reverse probabilities”,pr(e/h)are 1.I consider various possible ways to escape this quandary,none of which are without difficulties,concluding that a research policy allowing for unrestricted Academic Freedom is probably the best that we can hope for.

关键词： academic freedom Feminisim empirical evidence probability theory Bayesian probability scientific method causality causal reasoning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：