Relation Recognition(关系识别)研究综述
Relation Recognition 关系识别 - Video visual relation recognition aims at mining the dynamic relation instances between objects in the form of 〈 subject , predicate , object 〉 , such as “person1-towards-person2” and “person-ride-bicycle”. [1] The scene information in which people interact is also one of the important cues for social relation recognition (SRR). [2] Our suggested system consists of four consecutive sub-tasks, namely: (1) opinion-topic detection, (2) argumentative opinions identification, (3) argument components detection, and (4) argumentative relation recognition. [3] On the basis of image de-duplication, an image sentiment dictionary is constructed by correlation recognition between emoticons/images and texts. [4] In order to solve the mentioned problems, this paper will combine the algorithm that can highlight the syntactic structure in sentences and improve the accuracy of the model with the Algorithm that can highlight the contribution of words in sentences and the loss function level integration is carried out in the framework of small sample prototype network, so as to maximize the advantages of each algorithm and improve the accuracy –firstly, in the coding layer of the prototype network, we use the CNN algorithm which can highlight the important words in the sentences and the TreeLSTM algorithm which can parse the sentences in the text so that the syntactic relations between the words in the sentences can be acted on in the relation recognition, the sentences are coded together by two algorithms, then, the EUCLIDEAN distance loss is calculated by using this high quality coding and the prototype coding, finally, the traditional entity relation recognition model with Attention Mechanism is integrated into the loss function, further highlighting the decisive role of important words in text sentences in relation recognition and improving the generalization of the model. [5] Our evaluation schema indicates that the longitudinal cancer drug recognition pipeline delivers strong performance (named entity recognization for drugs and temporal: F1 = 95%; drug-temporal relation recognition: F1 = 90%). [6] However, in the current mainstream question answering methods, there is insufficient mining of the semantic information of question sentences, resulting in poor entity recognition and relation recognition effects, and many question answering methods rely on predefined rules, which have low transferability and high labor costs. [7] Macro discourse relation recognition is an important task of macro discourse analysis. [8] As a crucial task for video analysis, social relation recognition for characters not only provides semantically rich description of video content but also supports intelligent applications, e. [9] Discourse relation recognition is an important branch of NLP, which is helpful to solve many NLP downstream tasks. [10] Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse. [11] It is different from the traditional emotion and social relation recognition task. [12] In this work, we propose a gaze-aware graph convolutional network (GA-GCN) for social relation recognition, which targets discovering the context-aware social relation inference with gaze-aware attention. [13] Existing work of social relation recognition (SRR) mainly focuses on exploiting two or three types of features to recognize social relations without considering the relations between features. [14] In this article, we aim to study the problem of social relation recognition in an open environment. [15] In order to utilize this inter-dependency in tackling the challenges of visual relation recognition in videos, we propose a novel iterative relation inference approach for VidVRD. [16] The results show our method achieved excellent results on teaching notion retrieval and education correlation recognition. [17] In this paper, we propose a semantic three-stream network (STN) for social relation recognition, which learns discriminative features from facial images directly by exploiting semantic information effectively. [18] In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. [19] First, a correlation network is proposed for relation recognition task, which helps learn the complicated relations and common information of different modalities. [20] We introduce SpatialSense, a dataset specializing in spatial relation recognition which captures a broad spectrum of such challenges, allowing for proper benchmarking of computer vision techniques. [21] Driven by the 2010 i2b2/VA Challenge Evaluation, the relation recognition problem in EMRs has been studied by many researchers to address this important aspect of EMR information extraction. [22] It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR). [23] Implicit discourse relation recognition is a serious challenge in discourse analysis, which aims to understand and annotate the latent relations between two discourse arguments, such as temporal and comparison. [24] Implicit discourse relation recognition is the performance bottleneck of discourse structure analysis. [25] Experiments have shown that this approach to the relation recognition task is effective. [26] Second, how to mine the relationship between nuclearity and relation recognition effectively is another challenge. [27] On one hand, the actions and storylines in videos provide more important cues for social relation recognition. [28] Further more, the redundancy reduction strategy effectively reduces the size of the resulting knowledge graphs of hidden relation recognition on both synthetic and real-world knowledge graphs. [29] key words: social relation recognition, video analysis, deep learning, LSTM, attention mechanism. [30] To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images. [31] In order to improve the recognition function of the joint transform correlation, this paper describes the conception and application method of distorted images correlation recognition, makes Synthetic Discriminant Functions (SDF) in the computer and analyses the recognition results of rotation and scale distorted images. [32] So far there is no systematic work to investigate the influence of neural components on the performance of implicit discourse relation recognition. [33] Implicit causal relation recognition aims to identify the causal relation between a pair of arguments. [34] Our system consists of two components: a named entity recognition (NER) component and a relation recognition component. [35] The results of the simulation of correlation recognition of half-tone images using holographic correlation filters are presented. [36] We have made the training and validation set public and extendable for more tasks to facilitate future research on video object and relation recognition. [37] The model mainly consists of three modules: named entity recognition, relation recognition and entity relevance computation, which are implemented with conditional random fields, support vector machine and decision tree algorithms respectively. [38] Second, we mine the rules of semantic relation recognition from the correlations between dependency structures and semantic relations. [39]视频视觉关系识别旨在挖掘对象之间的动态关系实例，形式为〈主、谓、宾〉，如“person1-towards-person2”和“person-ride-bicycle”。 [1] 人们交互的场景信息也是社会关系识别（SRR）的重要线索之一。 [2] 我们建议的系统由四个连续的子任务组成，即：（1）观点主题检测，（2）争论观点识别，（3）争论成分检测，以及（4）争论关系识别。 [3] 在图像去重的基础上，通过表情/图像与文本之间的关联识别，构建图像情感词典。 [4] 为了解决上述问题，本文将突出句子中句法结构并提高模型准确率的算法与突出句子中词贡献的算法相结合，进行损失函数级别的整合。在小样本原型网络的框架下，最大限度地发挥各个算法的优势，提高准确率——首先，在原型网络的编码层，我们使用了CNN算法，可以突出句子中的重要词和TreeLSTM 算法可以解析文本中的句子，从而在关系识别中作用于句子中单词之间的句法关系，将句子通过两种算法编码在一起，然后使用此算法计算 EUCLIDEAN 距离损失高质量编码和原型编码，最后，传统的带有注意力机制的实体关系识别模型是集成到损失函数中，进一步突出了文本句子中重要词在关系识别中的决定性作用，提高了模型的泛化能力。 [5] 我们的评估模式表明，纵向癌症药物识别管道提供了强大的性能（药物和时间的命名实体识别：F1 = 95%；药物-时间关系识别：F1 = 90%）。 [6] 但是，目前主流的问答方式中，对问句语义信息的挖掘不足，导致实体识别和关系识别效果较差，而且很多问答方式依赖于预定义的规则，迁移性低，人工成本高。 . [7] 宏观语篇关系识别是宏观语篇分析的一项重要任务。 [8] 作为视频分析的关键任务，人物社会关系识别不仅提供了视频内容语义丰富的描述，而且支持智能应用，例如。 [9] 语篇关系识别是 NLP 的一个重要分支，有助于解决许多 NLP 下游任务。 [10] 隐式语篇关系识别（IDRR）旨在识别语篇中两个相邻句子之间的逻辑关系。 [11] 它不同于传统的情感和社会关系识别任务。 [12] 在这项工作中，我们提出了一种用于社会关系识别的注视感知图卷积网络 (GA-GCN)，其目标是通过注视感知注意力发现上下文感知社会关系推理。 [13] 现有的社会关系识别（SRR）工作主要集中在利用两种或三种类型的特征来识别社会关系，而不考虑特征之间的关系。 [14] 在本文中，我们旨在研究开放环境中的社会关系识别问题。 [15] 为了利用这种相互依赖性来解决视频中视觉关系识别的挑战，我们提出了一种新颖的 VidVRD 迭代关系推理方法。 [16] 结果表明，我们的方法在教学概念检索和教育关联识别方面取得了很好的效果。 [17] nan [18] nan [19] nan [20] nan [21] nan [22] nan [23] nan [24] nan [25] nan [26] nan [27] nan [28] nan [29] nan [30] nan [31] nan [32] nan [33] nan [34] nan [35] nan [36] nan [37] nan [38] nan [39]
Social Relation Recognition
The scene information in which people interact is also one of the important cues for social relation recognition (SRR). [1] As a crucial task for video analysis, social relation recognition for characters not only provides semantically rich description of video content but also supports intelligent applications, e. [2] It is different from the traditional emotion and social relation recognition task. [3] In this work, we propose a gaze-aware graph convolutional network (GA-GCN) for social relation recognition, which targets discovering the context-aware social relation inference with gaze-aware attention. [4] Existing work of social relation recognition (SRR) mainly focuses on exploiting two or three types of features to recognize social relations without considering the relations between features. [5] In this article, we aim to study the problem of social relation recognition in an open environment. [6] In this paper, we propose a semantic three-stream network (STN) for social relation recognition, which learns discriminative features from facial images directly by exploiting semantic information effectively. [7] On one hand, the actions and storylines in videos provide more important cues for social relation recognition. [8] key words: social relation recognition, video analysis, deep learning, LSTM, attention mechanism. [9] To bridge the domain gap, we propose a Multi-Granularity Reasoning framework for social relation recognition from images. [10]人们交互的场景信息也是社会关系识别（SRR）的重要线索之一。 [1] 作为视频分析的关键任务，人物社会关系识别不仅提供了视频内容语义丰富的描述，而且支持智能应用，例如。 [2] 它不同于传统的情感和社会关系识别任务。 [3] 在这项工作中，我们提出了一种用于社会关系识别的注视感知图卷积网络 (GA-GCN)，其目标是通过注视感知注意力发现上下文感知社会关系推理。 [4] 现有的社会关系识别（SRR）工作主要集中在利用两种或三种类型的特征来识别社会关系，而不考虑特征之间的关系。 [5] 在本文中，我们旨在研究开放环境中的社会关系识别问题。 [6] nan [7] nan [8] nan [9] nan [10]
Discourse Relation Recognition
Macro discourse relation recognition is an important task of macro discourse analysis. [1] Discourse relation recognition is an important branch of NLP, which is helpful to solve many NLP downstream tasks. [2] Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse. [3] In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. [4] It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR). [5] Implicit discourse relation recognition is a serious challenge in discourse analysis, which aims to understand and annotate the latent relations between two discourse arguments, such as temporal and comparison. [6] Implicit discourse relation recognition is the performance bottleneck of discourse structure analysis. [7] So far there is no systematic work to investigate the influence of neural components on the performance of implicit discourse relation recognition. [8]宏观语篇关系识别是宏观语篇分析的一项重要任务。 [1] 语篇关系识别是 NLP 的一个重要分支，有助于解决许多 NLP 下游任务。 [2] 隐式语篇关系识别（IDRR）旨在识别语篇中两个相邻句子之间的逻辑关系。 [3] nan [4] nan [5] nan [6] nan [7] nan [8]
Visual Relation Recognition
Video visual relation recognition aims at mining the dynamic relation instances between objects in the form of 〈 subject , predicate , object 〉 , such as “person1-towards-person2” and “person-ride-bicycle”. [1] In order to utilize this inter-dependency in tackling the challenges of visual relation recognition in videos, we propose a novel iterative relation inference approach for VidVRD. [2]视频视觉关系识别旨在挖掘对象之间的动态关系实例，形式为〈主、谓、宾〉，如“person1-towards-person2”和“person-ride-bicycle”。 [1] 为了利用这种相互依赖性来解决视频中视觉关系识别的挑战，我们提出了一种新颖的 VidVRD 迭代关系推理方法。 [2]
relation recognition task
It is different from the traditional emotion and social relation recognition task. [1] First, a correlation network is proposed for relation recognition task, which helps learn the complicated relations and common information of different modalities. [2] Experiments have shown that this approach to the relation recognition task is effective. [3]它不同于传统的情感和社会关系识别任务。 [1] nan [2] nan [3]
relation recognition aim
Video visual relation recognition aims at mining the dynamic relation instances between objects in the form of 〈 subject , predicate , object 〉 , such as “person1-towards-person2” and “person-ride-bicycle”. [1] Implicit causal relation recognition aims to identify the causal relation between a pair of arguments. [2]视频视觉关系识别旨在挖掘对象之间的动态关系实例，形式为〈主、谓、宾〉，如“person1-towards-person2”和“person-ride-bicycle”。 [1] nan [2]