Recognition Of Social Relations Research Articles

Principal relation component reasoning-enhanced social relation recognition

Principal relation component reasoning-enhanced social relation recognition

Structural reasoning for image-based social relation recognition

Modern societies are composed of complex structures that emerge from the relationships between individuals, and the comprehension of these arrangements has the potential to become a powerful tool for intelligent systems. Current image-based social relation recognition methods isolate specific information from the input to capture essential aspects defining these relationships. However, this is an inaccurate approach since the interaction between all these parts form an intricate structure, which is as valuable as the data each piece carries individually. Consequently, capturing this implicit social structure is essential to achieve the high-level reasoning required to identify relationships adequately. In this work, we propose a novel approach to interpret relationships based on three distinct scopes considering individual, relative, and general information. Additionally, it also takes into account prior knowledge and data dependencies between all these different social perspectives. The Social Knowledge Graph (SKG) is proposed based on these concepts, producing a representation capable of replicating the original social structure. This unique representation is exploited with the Social Graph Network (SGN) by employing specific feature aggregation strategies according to the information embedded into the graph. The performance of the proposed method was evaluated in well-known benchmarks for social relation recognition, achieving a new state-of-the-art. Finally, a deep analysis of the methodology and its main concepts is conducted, delivering results that support our interpretation of social relationships.

Learning Social Spatio-Temporal Relation Graph in the Wild and a Video Benchmark.

Social relations are ubiquitous and form the basis of social structure in our daily life. However, existing studies mainly focus on recognizing social relations from still images and movie clips, which are different from real-world scenarios. For example, movie-based datasets define the task as the video classification, only recognizing one relation in the scene. In this article, we aim to study the problem of social relation recognition in an open environment. To close the gap, we provide the first video dataset collected from real-life scenarios, named social relation in the wild (SRIW), where the number of people can be huge and vary, and each pair of relations needs to be recognized. To overcome new challenges, we propose a spatio-temporal relation graph convolutional network (STRGCN) architecture, utilizing correlative visual features to recognize social relations intuitively. Our method decouples the task into two classification tasks: person-level and pair-level relation recognition. Specifically, we propose a person behavior and character module to encode moving and static features in two explicit ways. Then we take them as node features to build a relation graph with meaningful edges in a scene. Based on the relation graph, we introduce the graph convolutional network (GCN) and local GCN to encode social relation features which are used for both recognitions. Experimental results demonstrate the effectiveness of the proposed framework, achieving 83.1% and 40.8% mAP in person-level and pair-level classification. Moreover, the study also contributes to the practicality in this field.

Unveiling Social Relations: Leveraging Interpersonal Similarity Learning for Social Relation Recognition

Unveiling Social Relations: Leveraging Interpersonal Similarity Learning for Social Relation Recognition

THE HONOR AND DIGNITY OF A MILITARY PERSONNEL AS THE OBJECT OF A CRIMINAL OFFENSE PROVIDED FOR BY ART. 435-1 OF THE CRIMINAL CODE OF UKRAINE

Abstract: The article deals with theoretical and practical issues of criminal legal protection of professional honor and dignity of military personnel. The armed aggression of the Russian Federation against Ukraine, along with other measures, required proper criminal legal protection of the rights and interests of military personnel who protect the territorial integrity and sovereignty of our country. The honor and dignity of servicemen needed such protection also. The work analyzes the main philosophical, ethical and legal approaches to determining such categories as honor and dignity of a person. The author emphasizes that the honor and dignity of a military personnel is professional honor and dignity. Their definition should be based on generally accepted definitions of these concepts. It is noted that the honor and dignity of any person, including their professional component, needs criminal legal protection. Based on the study of the main approaches to the definition of these social values and subjective rights of the person, their author's definition as objects of criminal law protection is given. Their unity is emphasized, but also stated that honor and dignity have certain differences. Based on the analysis of draft laws and laws that were adopted in the relevant field, it is noted that the criminal law protection of honor and dignity of a person should exist in our country. This corresponds to all the necessary principles of criminalization of socially dangerous acts. The author supports the position regarding the recognition of social relations as the object of a criminal offense. Based on this, the professional honor and dignity of military personnel is defined as the object of a criminal offense provided for in Article 435-1 of the Criminal Code of Ukraine. Based on the analysis of judicial practice, it is concluded that in most cases, together with dishonors, guilty persons make threats against military personnel, which confirms the correctness of the legislator's approach to include dishonors together with threats as an act in this offense. In most cases, these are threats of physical violence in various forms. Attention is drawn to certain shortcomings of the legislator's actions on the supplementation of criminal liability for this offense. Namely, due to a legislator's mistake, it is not Chapter XX "Criminal offenses against peace, human security and international legal order" added with Article 435-1, but Chapter XIX of the Special Part of the Criminal Code "Criminal offenses against the established order of military service (military criminal offences)". The subject of all criminal offenses provided for in this chapter, without exception, is special. This is directly defined in Part 1 of Art. 401 of the Criminal Code ("The concept of a military criminal offense"). But the subject of this criminal offense is a general one, that is, a natural compos mentis person who has reached the age of 16. This shortcoming should be corrected.

Multi-Level Transformer-Based Social Relation Recognition.

Social relationships refer to the connections that exist between people and indicate how people interact in society. The effective recognition of social relationships is conducive to further understanding human behavioral patterns and thus can be vital for more complex social intelligent systems, such as interactive robots and health self-management systems. The existing works about social relation recognition (SRR) focus on extracting features on different scales but lack a comprehensive mechanism to orchestrate various features which show different degrees of importance. In this paper, we propose a new SRR framework, namely Multi-level Transformer-Based Social Relation Recognition (MT-SRR), for better orchestrating features on different scales. Specifically, a vision transformer (ViT) is firstly employed as a feature extraction module for its advantage in exploiting global features. An intra-relation transformer (Intra-TRM) is then introduced to dynamically fuse the extracted features to generate more rational social relation representations. Next, an inter-relation transformer (Inter-TRM) is adopted to further enhance the social relation representations by attentionally utilizing the logical constraints among relationships. In addition, a new margin related to inter-class similarity and a sample number are added to alleviate the challenges of a data imbalance. Extensive experiments demonstrate that MT-SRR can better fuse features on different scales as well as ameliorate the bad effect caused by a data imbalance. The results on the benchmark datasets show that our proposed model outperforms the state-of-the-art methods with significant improvement.

청년세대의 주관적 계층인식 영향요인 분석

This study empirically analyzed the influence factors of the subjective social status of the youth generation in our Korean society using the data of the ‘2020 Social Integration Survey’.
 To verify the relative difference in influence on subjective social status, which is a dependent variable, individual socioeconomic factors, individual emotional factors, and social relation recognition factors were selected as independent variables.
 The mean analysis and reliability analysis of variables were performed through frequency analysis and reliability analysis of SPSS 25, and hierarchical regression analysis was performed in which independent variables were input step by step to analyze the factors affecting subjective social status.
 As a result of the study, it was confirmed that the individual's emotional factors and social relationship recognition factors had a greater influence than the individual socioeconomic factors, which were input as independent variables. From these results, social support and cohesion should be created so that our young generation can have individual emotional stability, social support, and awareness of being in the community rather than comparing themselves to others.

Learning Social Relationship From Videos via Pre-Trained Multimodal Transformer

As a crucial task for video analysis, social relation recognition from characters provides intelligent applications with great potential to better understand the behaviors or emotions of human beings. Most existing methods mainly focus on training models from a large amount of labeled data. However, labeling social relations in videos is time-consuming. To solve this problem, we propose a Pre-trained Multimodal Feature Learning (PMFL) framework for self-supervised learning from unlabeled video data, and then transfer the pre-trained PMFL to downstream social relationship recognition task. First, the space-time interaction between visual instances, and cross-modal interaction between visual and textual information provide important cues for social relation understanding. To incorporate these cues, we design a Multimodal Instance Interaction Transformer (MIIT), which consists of two Transformers to capture intra-modal and cross-modal information interaction, respectively. Second, to better endow PMFL with the capability of learning visual and textual semantic features, we pre-train it via two tasks: Masked Action Feature Regression (MAFR) and Masked Object Label Classification (MOLC). These tasks can help learn both intra-modal and cross-modal semantic information. After fine-tuning PMFL from pre-trained parameters, it achieves the state-of-the-art results on a public benchmark.

SRR-LGR: Local–Global Information-Reasoned Social Relation Recognition for Human-Oriented Observation

People’s interactions with each other form the social relations in society. Understanding human social relations in the public space is of great importance for supporting the public administrations. Recognizing social relations through visual data captured by remote sensing cameras is one of the most efficient ways to observe human interactions in a public space. Generally speaking, persons in the same scene tend to know each other, and the relations between person pairs are strongly correlated. The scene information in which people interact is also one of the important cues for social relation recognition (SRR). The existing works have not explored the correlations between the scene information and people’s interactions. The scene information has only been extracted on a simple level and high level semantic features to support social relation understanding are lacking. To address this issue, we propose a social relation structure-aware local–global model for SRR to exploit the high-level semantic global information of the scene where the social relation structure is explored. In our proposed model, the graph neural networks (GNNs) are employed to reason through the interactions (local information) between social relations and the global contextual information contained in the constructed scene-relation graph. Experiments demonstrate that our proposed local–global information-reasoned social relation recognition model (SRR-LGR) can reason through the local–global information. Further, the results of the final model show that our method outperforms the state-of-the-art methods. In addition, we have further discussed whether the global information contributes equally to different social relations in the same scene, by exploiting an attention mechanism in our proposed model. Further applications of SRR for human-observation are also exploited.

Socializing the Videos: A Multimodal Approach for Social Relation Recognition

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.g., video retrieval and visual question answering. Unfortunately, due to the semantic gap between visual and semantic features, traditional solutions may fail to reveal the accurate relations among characters. At the same time, the development of social media platforms has now promoted the emergence of crowdsourced comments, which may enhance the recognition task with semantic and descriptive cues. To that end, in this article, we propose a novel multimodal-based solution to deal with the character relation recognition task. Specifically, we capture the target character pairs via a search module and then design a multistream architecture for jointly embedding the visual and textual information, in which feature fusion and attention mechanism are adapted for better integrating the multimodal inputs. Finally, supervised learning is applied to classify character relations. Experiments on real-world data sets validate that our solution outperforms several competitive baselines.

视频社会关系识别的多尺度图推理模型

视频社会关系识别的多尺度图推理模型

Gaze-Aware Graph Convolutional Network for Social Relation Recognition

Social relation, as the basic relation in our daily life, is vital for social action analysis. However, how to learn the social feature between people is still not tackled. 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. To predict the gaze direction, we apply a convolutional network trained with gaze direction loss. Then, we build a graph convolutional inference module, which is a two-stream graph inference with both gaze-aware attention and distance-aware attention. The attention can pick up relevant context objects for context-aware representation. We further introduce additional scene features and construct a multiple feature fusion module, which can adaptively learn social relation representation from both scene feature and context-aware feature. Extensive experiments on the PISC and the PIPA datasets demonstrate that our GA-GCN can find interesting contextual objects and achieves state-of-the-art performances.

Attentive Sequences Recurrent Network for Social Relation Recognition from Video

Attentive Sequences Recurrent Network for Social Relation Recognition from Video

Semantic three-stream network for social relation recognition

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. Specifically, we employ a semantic augmentation structure to extract enriched semantic features from original face images, where a Siamese network is used to extract features from a pair of face images. We concatenate features of three streams for social relation recognition. Unlike most existing relation recognition methods, our method explicitly uses semantic information to discover the social relation. The proposed semantic augmentation structure can be easily embedded into the off-the-shell deep neural network, which leads to a powerful and flexible semantic augmentation network. Experimental results show that our proposed method outperforms the state-of-the-arts.

MARGINALISASI MASYARAKAT ADAT

Entering the 21st century this far, discourses of adat communities is resurfacing. They are presented because the peoples‟ basic rights and ownership over agricultural resources are disturbed and trembled by the greediness of the ruling regime. In the midst of their powerlessness they are expelled from their socio-cultural attachment, and even more they are wiped out from the population map of a nation. But every regime always bring their own interpretation that leads to the elimination of the recognition of indigenous peoples. Transition of regime to Indonesians does not necessarily change the mentality and the culture that have been firmly embedded (embeddedness), but rather the new regime acts, without any embarrassment and awkward feelings, as successor in the marginalization of the adat communities and inherently their agrarian resources. Actually it is a "misguided thought" of the regime over their own adat communities. The implication therefore indicates that it is a necessity that the existence of indigenous peoples means, at the same time, the recognition of social relations in the society, the nation and the state.

MULHERES NA GERÊNCIA EM TECNOLOGIA DA INFORMAÇÃO: ANÁLISE DE EXPRESSÕES DE EMPODERAMENTO

The growing number of women in the labor market is defining new contours of gender relations and power in businesses. These point to movements and processes for empowerment of women together with a range of welfare and recognition of social relations, presenting a challenge to patriarchal relations and maintenance of male privileges. This is an increased awareness by women, who have the skill and competence, to produce, create, manage and transform their own life and surroundings while acting out their own history. This work analyzed expressions of empowerment of women who manage information technology in companies in Belo Horizonte, Minas Gerais, Brazil with an exploratory-descriptive field research with semi-structured interviews of 12 female managers. Results showed that some managers express more visible signs of empowerment than others. This was expressed through an awareness and building of a social identity, with motivation and mobilization of their potential to occupy a managerial position.