Personal Image Research Articles

Message patterns of online gender-based humor discriminatory practices biases stereotyping and disempowering tools through discourse analysis

This study explored the message patterns of gender-based humor in social media in different layers of discriminatory practices against certain genders, language biases against women and LGBT including elements of stereotyping and disempowering tools against the personal images of subordinate genders. This research used discourse analysis based on the mapped-out online posts and comments of the fourteen (14) profiles of individuals and extracted their important testimonies based on the collected online gender-based humor to elicit the message patterns. Gender-based humor online enhanced the language use in creating messages that express biases towards women and the LGBT. Humor has both implicit and explicit messages that stereotype women and LGBT as weak and slow. These senses of humor also disempower the women and LGBT's personal images as groups who are easily dominated or are cowards. As asserted, gender-based humor posed a threat to community as it highlights hierarchy-enhancing social roles. Gender-based humor in social media appeared as a mainstreamed form of social differentiation.

Full-view salient feature mining and alignment for text-based person search

Text-based person search aims to retrieve relevant person images from a large database given textual queries. However, single-view limitation of surveillance cameras and cross-modal heterogeneity still remain challenging open issues. To address these, we propose a Full-view Salient Feature Mining Network (FLAN) to improve text-image matching in this task. Our FLAN introduces two key innovations. First, the Diffusion-based Full-view Image Augmentation generates informative full-view data from a single image to simulate human visual observation and learn view-invariant features. Second, the Dual-max Text Attention module optimizes spatial and channel-wise text attentions to extract the most discriminative words characterizing the person. Together, these innovations handle insufficient, imbalanced, and heterogeneous data for more accurate matching. Extensive experiments on three text-based person search datasets, CUHK-PEDES, ICFG-PEDES and RSTPReid, demonstrate superior performance of our FLAN with improved robustness and generalization.

Person Image Generation Guided by Posture, Expression and Illumination

Person pose transfer has shown great application prospects and potential in recent years. However, this research mainly focuses on posture transfer and does not pay much attention to other human body attributes. In addition, there are problems such as blurred facial expressions and inconsistent facial texture. In order to solve these problems and improve user experience, this paper proposes a multi-feature transfer person image generation model, which combines facial expression transfer and pose transfer and completes the lighting reconstruction of person images. Our model can simultaneously migrate and supplement facial expressions while migrating a person’s posture, ensuring the consistency of characters’ overall migration. Our model can freely switch personal body lighting to adapt to different scenes, greatly improving the user experience. The experimental results show that our method’s preservation rate for face identity is improved by nearly 35% compared with the benchmark, and the similarity index for the overall person body is improved by nearly 14%. The evaluation results indicate that our method performs well in person posture transfer. The experimental results show that the proposed method is superior to the existing algorithms in person posture transformation.

THE EFFECT OF DIGITAL DRAMA ON EFL STUDENTS’ SKILLS IN THE INTERCULTURAL COMMUNICATION COURSE AT THE UNIVERSITY

<p>Intercultural communication is concerned with the sharing of information between different cultures and social groups, including individuals with religious, social, ethnic, and educational backgrounds. Cultural factors have a great impact on how people conduct diplomatic, economic, and educational activities in the era of international integration. The application of innovative methods in teaching Intercultural communication courses at universities is extremely necessary in order to break down cultural barriers and build awareness of cultural norms, as well as to enhance self-awareness, language skills and communication skills in a multicultural environment. At the same time, it helps learners to explore, preserve and promote the national cultural identity. The theatrical (drama) technique has been recognized as one of the powerful means of education and propaganda. One of the proposed methods is integrating drama in the context of digital transformation. This study aims to investigate the effectiveness of applying theatrical integration and online teaching tools via Zoom in teaching the Intercultural module to create a borderless cultural class. The quantitative method was conducted through experimental convention and questionnaires with the participation of students majoring in English at a university in the Mekong Delta, Vietnam. Specific steps have been taken to improve intercultural communication skills and intercultural interaction will enhance the ability to communicate and promote personal image as well as showcase national culture. The research has shown that using the digital drama model in the classroom as a teaching method not only helps learners academically but also provides knowledge about socio-cultural development.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/lit/0057/a.php" alt="Hit counter" /></p>

Subjective benefits from wearing self-fitting over-the-counter hearing aids in the real world.

In 2022, the US Food and Drug Administration enacted final regulations to establish the category of over-the-counter (OTC) hearing aids aimed at reducing barriers to access hearing health care for individuals with self-perceived mild to moderate hearing loss. However, given the infancy of this device category, the effectiveness of OTC hearing aids in real-world environments is not yet well understood. To gain insights into the perceived benefit of self-fitting OTC hearing aids, a two-pronged investigation was conducted. In the primary investigation, 255 active users of a self-fitting OTC hearing aid were surveyed on their perceived benefit using an abridged form of the Satisfaction with Amplification in Daily Living (SADL) scale. The mean global (4.9) and subscale scores (Positive Effect (PE): 4.3; Negative Features (NF): 4.3; Personal Image (PI): 6.1) were within the range of those previously reported for users of prescription hearing aids. In the secondary investigation, 29 individuals with self-reported hearing impairment but no prior experience with the investigational self-fitting OTC hearing aids used the devices and reported their perceived benefit and satisfaction following short-term usage. For this prospective group, the global SADL (5.4) and subscale scores (PE: 4.8; NF: 4.9; PI: 6.5) following a minimum of 10 weeks of real-world use were also within the range of those previously reported for traditional hearing aid users. In addition, this prospective group was also asked quality of life questions which assessed psychological benefits of hearing aid use. Responses to these items suggest hearing aid related improvements in several areas spanning emotional health, relationships at home and at work, social life, participation in group activities, confidence and feelings about one's self, ability to communicate effectively, and romance. Converging data from these investigations suggest that self-fitting OTC hearing aids can potentially provide their intended users with a level of subjective benefit comparable to what prescription hearing aid users might experience.

The Subversive AI Acceptance Scale (SAIA-8): A Scale to Measure User Acceptance of AI-Generated, Privacy-Enhancing Image Modifications

To resist government and corporate use of facial recognition to surveil users through their personal images, researchers have created privacy-enhancing image filters that use adversarial machine learning. These "subversive AI'' (SAI) image filters aim to defend users from facial recognition by distorting personal images in ways that are barely noticeable to humans but confusing to computer vision algorithms. SAI filters are limited, however, by the lack of rigorous user evaluation that assess their acceptability. We addressed this limitation by creating and validating a scale to measure user acceptance --- the SAIA-8. In a three-step process, we apply a mixed-methods approach that closely adhered to best practices for scale creation and validation in measurement theory. Initially, to understand the factors that influence user acceptance of SAI filter outputs, we interviewed 15 participants. Interviewees disliked extant SAI filter outputs because of a perceived lack of usefulness and conflicts with their desired self-presentation. Using insights and statements from the interviews, we generated 106 potential items for the scale. Employing an iterative refinement and validation process with 245 participants from Prolific, we arrived at the SAIA-8 scale: a set of eight items that capture user acceptability of privacy-enhancing perturbations to personal images, and that can aid in benchmarking and prioritizing user acceptability when developing and evaluating new SAI filters.

Observation or Insight? Unraveling the Impact of Characters' Image on Interpersonal Communication in Contemporary Chinese TV Series

A salient determinant of effective interpersonal communication resides in the cultivation of a positive personal image. The intricate interplay between self-image, individual behaviors, and communication abilities highlights the far-reaching influence of personal perception on social interactions. In the contemporary landscape of Chinese television series, exemplified by the production ‘Nothing but Thirty’, a deliberate effort is made to depict characters that embody the evolving roles of women in modern society. Drawing from relevant literature, this study conducts a comprehensive analysis of characters’ image in ‘Nothing but Thirty’, examining content such as appearance, language, non-verbal cues (body language), and varied scene settings. Adopting a rigorous qualitative case study methodology and leveraging content analysis grounded in the coordinated management of meaning theory and social exchange theory, the research aims to dissect the nuanced dynamics of interpersonal communication as portrayed in the series. The findings illuminate that the dynamic characterizations in contemporary Chinese TV series function as reflective mirrors, capturing the intricate layers of modern interpersonal communication and societal intricacies. This narrative analysis extends beyond the purview of conventional storytelling, offering a scholarly exploration of the intricate fabric of human interaction.

Exploring the attitudes and behavioral intentions of marathon racers: a cross-national inquiry

ABSTRACT In light of the anticipated expansion of marathon events on a global stage, this study collected survey data from marathon participants who traveled to two prominent events in Chicago, USA, and Xiamen, China. On average, Chinese marathon travelers had higher scores than their American counterparts in all major constructs, including embodied experiences, destination personality, perceived event image, and behavioral intentions. This study adopted the Stimulus-Organism-Response framework to propose a conceptual model that illustrates how embodied experience affects destination personality, event image, and intentions to participate in a sports event. Both an exploratory factor analysis (EFA) and the PLS-SEM technique were then used to analyze the data. The empirical findings demonstrated a positive impact of embodied experience (a stimulus) on destination personality and event image (organisms). Furthermore, both destination personality and event image positively impacted the behavioral intentions of marathon racers (a response). Both theoretical implications and managerial implications are discussed.

Brain functional connectivity correlates of anomalous interaction between sensorily isolated monozygotic twins.

This study examined brain functional connectivity (FC) changes associated with possible anomalous interactions between sensorily isolated monozygotic (MZ) twins. Brain FC was estimated using the Steady State Visual Evoked Potential-Event Related Partial Coherence (SSVEP-ERPC) methodology. Five twin pairs served twice as participants, with an average interval between sessions of 67 days. In each recording session, one twin, the Sender, viewed a randomized set of 50 general images and 50 personally relevant images, while the other twin, the Receiver, viewed a static personally relevant image for the entire duration of the session. Images appeared on the Sender screen for 1.0 s, with the interval between successive images varied randomly between 4.0 and 8.0 s. Receiver FC changes were calculated based on the appearance times of the images as viewed by the Sender. It was hypothesized that anomalous interactions would be indicated by statistically significant Receiver FC changes when those changes are determined using the Sender image appearance times. For each twin serving as Receiver, FC components were separately analyzed for the 50 general and the 50 personal images, yielding 38 observations (19 twin pairs by 2 conditions). The hypothesis was confirmed in that 11 of the 38 observations yielded statistically significant Receiver FC increases or decreases at the p < 0.01 level only when trials were synchronized to the Sender image appearance times. Overall, this effect was significant at the p = 4 × 10-8 Df = 175. To the best of our knowledge, this is the first study reporting statistically significant FC changes indicative of anomalous interactions between two sensorily isolated individuals.

Text-based person search via cross-modal alignment learning

Text-based person search aims to use text descriptions to search for corresponding person images. However, due to the obvious pattern differences in image and text modalities, it is still a challenging problem to align the two modalities. Most existing approaches only consider semantic alignment within a global context or partial parts, lacking consideration of how to match image and text in terms of differences in model information. Therefore, in this paper, we propose an efficient Modality-Aligned Person Search network (MAPS) to address this problem. First, we suppress image-specific information by image feature style normalization to achieve modality knowledge alignment and reduce information differences between text and image. Second, we design a multi-granularity modal feature fusion and optimization method to enrich the modal features. To address the problem of useless and redundant information in the multi-granularity fused features, we propose a Multi-granularity Feature Self-optimization Module (MFSM) to adaptively adjust the corresponding contributions of different granularities in the fused features of the two modalities. Finally, to address the problem of information inconsistency in the training and inference stages, we propose a Cross-instance Feature Alignment (CFA) to help the network enhance category-level generalization ability and improve retrieval performance. Extensive experiments demonstrate that our MAPS achieves state-of-the-art performance on all text-based person search datasets, and significantly outperforms other existing methods.

Are deep learning classification results obtained on CT scans fair and interpretable?

Following the great success of various deep learning methods in image and object classification, the biomedical image processing society is also overwhelmed with their applications to various automatic diagnosis cases. Unfortunately, most of the deep learning-based classification attempts in the literature solely focus on the aim of extreme accuracy scores, without considering interpretability, or patient-wise separation of training and test data. For example, most lung nodule classification papers using deep learning randomly shuffle data and split it into training, validation, and test sets, causing certain images from the Computed Tomography (CT) scan of a person to be in the training set, while other images of the same person to be in the validation or testing image sets. This can result in reporting misleading accuracy rates and the learning of irrelevant features, ultimately reducing the real-life usability of these models. When the deep neural networks trained on the traditional, unfair data shuffling method are challenged with new patient images, it is observed that the trained models perform poorly. In contrast, deep neural networks trained with strict patient-level separation maintain their accuracy rates even when new patient images are tested. Heat map visualizations of the activations of the deep neural networks trained with strict patient-level separation indicate a higher degree of focus on the relevant nodules. We argue that the research question posed in the title has a positive answer only if the deep neural networks are trained with images of patients that are strictly isolated from the validation and testing patient sets.

CSR fit with CEO image: Unpacking its influence on stakeholder engagement and authenticity perception

ABSTRACT This study explores the significant role of Chief Executive Officers (CEOs) in communicating a corporation’s commitment to Social Responsibility (CSR) and their influence on consumer engagement in CSR initiatives. This study introduces a conceptual model that integrates the sense-making approach and social exchange theory within the CSR domain. The model posits a positive correlation between a CEO’s ethical leadership, their personal image congruence with CSR initiatives, and consumer engagement in CSR. Central to this study is the empirical testing of the model, which revealed that CSR authenticity and Consumer-Company Identification (CCI) significantly mediate the relationship between CEO attributes and consumer engagement. Data collected from an online survey with 988 respondents supported that CEOs who are perceived as ethically sound and whose personal image aligns with CSR initiatives tend to foster higher levels of consumer engagement through enhanced perceptions of CSR authenticity and strengthened CCI. These findings offer new insights into the dynamics of CEO influence on CSR communication and consumer engagement.

Trends in the study of the image of a modern man in the art of Russia at the beginning of the XXI century

The subject of the study is the consideration of modern trends in the study of fine art, in particular Russian portrait and narrative painting of the beginning of the XXI century. The object is scientific works related to various fields of the humanities, namely: art history, aesthetics, anthropology, sociology, philosophy, psychology, etc. Using their material, it is possible to show not only the difference in approaches to understanding the essence and role of the image of a modern person in the art process, but also to demonstrate the specifics of the artistic interpretation of this figurative system in the context of art criticism, cultural and philosophical issues. Such a study allows us to systematize existing ideas about the peculiarities of the development of anthropological subjects in the mainstream of contemporary fine art in modern Russia, to identify the main directions. The main methodological basis of the study was a systematic approach that allowed us to consider contemporary Russian art as a single system. This helped to compare the approaches of modern researchers analyzing the place and role of the human image in it. Through the postmodern paradigm of perception and interpretation of anthropological topics, to identify the essential features of the ideas of Russian scientists. Methods of art historical analysis were also used. The starting point of the research is that at the moment in Russian science there is a body of art history and cultural studies devoted to the development of contemporary art in the country. However, there are no works summarizing the existing experience of scientists and defining the main directions of scientific thought. In light of this, the scientific novelty of the article lies in expanding the understanding of the methodological apparatus necessary for the analysis of the anthropological trend in the visual arts of our time, which is a promising path for future research in this area. The most demanded conceptual philosophical and historical-cultural foundations for the analysis and solution of practical problems in the field of contemporary art in relation to the representation of the human image are identified. Empirical research material has been collected and analyzed.

Selective and Orthogonal Feature Activation for Pedestrian Attribute Recognition

Pedestrian Attribute Recognition (PAR) involves identifying the attributes of individuals in person images. Existing PAR methods typically rely on CNNs as the backbone network to extract pedestrian features. However, CNNs process only one adjacent region at a time, leading to the loss of long-range inter-relations between different attribute-specific regions. To address this limitation, we leverage the Vision Transformer (ViT) instead of CNNs as the backbone for PAR, aiming to model long-range relations and extract more robust features. However, PAR suffers from an inherent attribute imbalance issue, causing ViT to naturally focus more on attributes that appear frequently in the training set and ignore some pedestrian attributes that appear less. The native features extracted by ViT are not able to tolerate the imbalance attribute distribution issue. To tackle this issue, we propose two novel components: the Selective Feature Activation Method (SFAM) and the Orthogonal Feature Activation Loss. SFAM smartly suppresses the more informative attribute-specific features, compelling the PAR model to capture discriminative features from regions that are easily overlooked. The proposed loss enforces an orthogonal constraint on the original feature extracted by ViT and the suppressed features from SFAM, promoting the complementarity of features in space. We conduct experiments on several benchmark PAR datasets, including PETA, PA100K, RAPv1, and RAPv2, demonstrating the effectiveness of our method. Specifically, our method outperforms existing state-of-the-art approaches by GRL, IAA-Caps, ALM, and SSC in terms of mA on the four datasets, respectively.

High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VI-ReID) aims to retrieve images of the same persons captured by visible (VIS) and infrared (IR) cameras. Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space due to the large modality discrepancy between VIS and IR images. To address the above problems, we propose a novel high-order structure based middle-feature learning network (HOS-Net) for effective VI-ReID. Specifically, we first leverage a short- and long-range feature extraction (SLE) module to effectively exploit both short-range and long-range features. Then, we propose a high-order structure learning (HSL) module to successfully model the high-order relationship across different local features of each person image based on a whitened hypergraph network. This greatly alleviates model collapse and enhances feature representations. Finally, we develop a common feature space learning (CFL) module to learn a discriminative and reasonable common feature space based on middle features generated by aligning features from different modalities and ranges. In particular, a modality-range identity-center contrastive (MRIC) loss is proposed to reduce the distances between the VIS, IR, and middle features, smoothing the training process. Extensive experiments on the SYSU-MM01, RegDB, and LLCM datasets show that our HOS-Net achieves superior state-of-the-art performance. Our code is available at https://github.com/Jaulaucoeng/HOS-Net.

Multi-Prompts Learning with Cross-Modal Alignment for Attribute-Based Person Re-identification

The fine-grained attribute descriptions can significantly supplement the valuable semantic information for person image, which is vital to the success of person re-identification (ReID) task. However, current ReID algorithms typically failed to effectively leverage the rich contextual information available, primarily due to their reliance on simplistic and coarse utilization of image attributes. Recent advances in artificial intelligence generated content have made it possible to automatically generate plentiful fine-grained attribute descriptions and make full use of them. Thereby, this paper explores the potential of using the generated multiple person attributes as prompts in ReID tasks with off-the-shelf (large) models for more accurate retrieval results. To this end, we present a new framework called Multi-Prompts ReID (MP-ReID), based on prompt learning and language models, to fully dip fine attributes to assist ReID task. Specifically, MP-ReID first learns to hallucinate diverse, informative, and promptable sentences for describing the query images. This procedure includes (i) explicit prompts of which attributes a person has and furthermore (ii) implicit learnable prompts for adjusting/conditioning the criteria used towards this person identity matching. Explicit prompts are obtained by ensembling generation models, such as ChatGPT and VQA models. Moreover, an alignment module is designed to fuse multi-prompts (i.e., explicit and implicit ones) progressively and mitigate the cross-modal gap. Extensive experiments on the existing attribute-involved ReID datasets, namely, Market1501 and DukeMTMC-reID, demonstrate the effectiveness and rationality of the proposed MP-ReID solution.

An Empirical Study of CLIP for Text-Based Person Search

Text-based Person Search (TBPS) aims to retrieve the person images using natural language descriptions. Recently, Contrastive Language Image Pretraining (CLIP), a universal large cross-modal vision-language pre-training model, has remarkably performed over various cross-modal downstream tasks due to its powerful cross-modal semantic learning capacity. TPBS, as a fine-grained cross-modal retrieval task, is also facing the rise of research on the CLIP-based TBPS. In order to explore the potential of the visual-language pre-training model for downstream TBPS tasks, this paper makes the first attempt to conduct a comprehensive empirical study of CLIP for TBPS and thus contribute a straightforward, incremental, yet strong TBPS-CLIP baseline to the TBPS community. We revisit critical design considerations under CLIP, including data augmentation and loss function. The model, with the aforementioned designs and practical training tricks, can attain satisfactory performance without any sophisticated modules. Also, we conduct the probing experiments of TBPS-CLIP in model generalization and model compression, demonstrating the effectiveness of TBPS-CLIP from various aspects. This work is expected to provide empirical insights and highlight future CLIP-based TBPS research.

Dummy's Guide to using Instagram like a REAL Journalist

Journalism is an ever-growing industry. In the digital age of the 21st century, social media platforms like Facebook and Instagram (Meta) have become a place for engaging with audiences in an attempt to humanize news publications. Until the summer of 2023, social media platforms were open spaces for the accessible sharing of news. The reliance of journal publications on social media to interact with the audience not only built a boundary between personal and professional image for journalists but also made journalists’ interactions with their audiences very passive. With the changes implemented by Meta in response to Bill C-18, which took away news organizations’ accounts from their social media platforms in Canada, the process of engaging with audiences and presenting news changed to become more journalist-oriented. Furthermore, journalists participated more actively as they had to present themselves by using their personal accounts. Now that the line between professional and personal identity on social media is blurred, it provides space for discourse on how a journalist brands themselves on a platform that is silencing their work. My study investigates a local independent student news publication’s editorial board practices to observe how student journalists are resisting the news-sharing ban Meta has introduced. I found that the platform’s policy changes were mostly superficial, and loopholes were easy to find and exploit. The findings were used to make an Instagram account in the form of a guide for student journalists, documenting the process of bypassing the content-sharing ban.

MP2PMatch: A Mask-guided Part-to-Part Matching network based on transformer for occluded person re-identification

Occluded person re-identification (ReID) remains challenging due to the misaligned body parts. Existing works, mainly utilizing extra clues, excel in predicting holistic person images but falter when confronting substantial occlusion. This paper proposes a transformer-based Mask-guided Part-to-Part Matching (MP2PMatch) network for fine-grained matching. Firstly, the Consistency Occlusion Augmentation (COA) processes holistic person images and corresponding body part masks to construct occluded “image-mask” pairs. Next, we introduce learnable part tokens to capture semantic features of various body parts, performing “pull close” and “push apart” operations based on identity labels and part visibility, ensuring the one-to-one correspondence between part features and body parts. Additionally, the proposed Body Region Attention (BRA) utilizes the overall attention on body regions to guide the network to overcome interference from both occlusion and background. Extensive experiments demonstrate that MP2PMatch achieves exceptional occluded ReID performance.

THE RIGHT TO ONE'S IMAGE, AS A RIGHT OF THE HUMAN PERSONALITY JURISPRUDENCE

Through a theoretical and practical approach, the study presents the main regulations of the right to image, as a personal non-patrimonial right: the Romanian Civil Code, the Romanian Constitution, and the new legislation in the field of personal data. The study presents practical issues such as jurisprudential solutions in Romania regarding the violation of the right to image in the online environment (Facebook, Instagram), television shows, and press articles. Also, the study deals with the much-disputed issues of using the person's image in the procedures for sanctioning the employees, analyzing how the National Authority for Supervision of Personal Data Processing (N.A.S.P.D.P.) from Romania sanctioned the abuses of the employers. The case studies and the analysis of European and Romanian legislation are also useful to students, theoreticians, and legal practitioners.