Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends最新文献

{"title":"Speeding it Up: Perception of High-Frame Rate Videos","authors":"A. Bovik","doi":"10.1145/3423268.3423585","DOIUrl":"https://doi.org/10.1145/3423268.3423585","url":null,"abstract":"Modern streaming video providers continuously seek to improve consumer experiences by delivering higher-quality, denser content. An important direction that bears study is high-frame rate (HFR) videos, which present unique problems involving balances between frame rate, video quality, and compression. I will describe new large-scale perceptual studies that we have conducted that are focused on these issues. I will also describe new computational video quality models that address highly practical questions, such as frame rate selection versus compression, and how to combine space-time sampling with compression. My hopes are that these contributions will help further advance the global delivery of HFR video content","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133636600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EVA: An Explainable Visual Aesthetics Dataset","authors":"Chen Kang, G. Valenzise, F. Dufaux","doi":"10.1145/3423268.3423590","DOIUrl":"https://doi.org/10.1145/3423268.3423590","url":null,"abstract":"Assessing visual aesthetics has important applications in several domains, from image retrieval and recommendation to enhancement. Modern aesthetic quality predictors are data driven, and leverage the availability of large annotated datasets to train accurate models. However, labels in existing datasets are often noisy, incomplete or they do not allow more sophisticated tasks such as understanding why an image looks beautiful or not to a human observer. In this paper, we propose an Explainable Visual Aesthetics (EVA) dataset, which contains 4070 images with at least 30 votes per image. Compared to previous datasets, EVA has been crowdsourced using a more disciplined approach inspired by quality assessment best practices. It also offers additional features, such as the degree of difficulty in assessing the aesthetic score, rating for 4 complementary aesthetic attributes, as well as the relative importance of each attribute to form aesthetic opinions. A statistical analysis on EVA demonstrates that the collected attributes and relative importance can be linearly combined to explain effectively the overall aesthetic mean opinion scores. The dataset, made publicly available, is expected to contribute to future research on understanding and predicting visual quality aesthetics.","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117137326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Gender Stereotypes and Electoral Success from Visual Self-presentations of Politicians in Social Media","authors":"Danni Chen, Kunwoo Park, Jungseock Joo","doi":"10.1145/3423268.3423583","DOIUrl":"https://doi.org/10.1145/3423268.3423583","url":null,"abstract":"Social media have been widely used as a platform for political communication, promoting firsthand dialogue between politicians and the public. This paper studies the role of visual self-presentation in social media in political campaigns with a primary focus on gender stereotypical cues exhibited in Facebook timeline posts of 562 candidates in the 2018 U.S. general elections. We train a convolutional neural network (CNN) that infers gender stereotypes from the photographs based on crowdsourced annotations. Using regression analysis, we find that masculine traits are predictive factors for winning elections for both gender and parties. In contrast, feminine traits are not correlated with electoral success. Prediction experiments show that the visual traits on gender stereotypes can predict the election outcomes with an accuracy of 0.739, which was better than the performance (0.724) of making a direct prediction from the raw photographs. Our study demonstrates that the automated visual content analysis can reliably measure subtle, emotional, and subjective personal trait dimensions from political images, thereby enabling systematic investigations on multi-modal political communication via social media.","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115797256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rating Distribution and Personality Prediction for ImageAesthetics Assessment","authors":"Weisi Lin","doi":"10.1145/3423268.3423588","DOIUrl":"https://doi.org/10.1145/3423268.3423588","url":null,"abstract":"Aesthetics has been an area of intensive interests and continuing exploration for long in multiple disciplines, such as philology, psychology, arts, photography, computer graphics, media, industrial design, and so on. Objective image aesthetic assessment (IAA) is related to three major considerations. First of all, technical quality assessment (TQA) of images still plays an important role in general, because basic visual features (e.g., contrast, brightness, colorfulness and semantic information) definitely influence humans' perception and experience. TQA has been already relatively better developed during the past two decades, so to be successful, IAA needs to focus more on the other two considerations that are special to it. The first consideration special to IAA is generic IAA (GIAA) which deals with aesthetic factors common to a typical human being or user, with examples of Rule of Thirds, symmetry, depth of field, object saliency, color Harmony, etc. The last special consideration is personalized IAA (PIAA) that is crucial in enabling many practical tasks, because \"beauty is in the eye of the beholder\". Compared with mere TQA, IAA is expected to be much more individualized, and machine learning provides an effective mean for the related tasks to be tackled. This talk will therefore introduce and discuss two issues particularly significant to PIAA: rating distribution prediction and personality-assisted aesthetic assessment. For the former, objective prediction will be demonstrated to be able to predict the subjective rating distribution (rather just the mean opinion score (MOS) in most existing TQA methods), since PIAA may have higher diversity of opinions from subjects (even with twin-peak distribution), especially for abstract art images. In such situations, a simple MOS estimation alone is far from the real opinions of aesthetics. For the latter, viewers/users of different personality (determined by the oft-used Big-Five scheme) have different preferences toward various categories of images. Personality prediction and its use in PIAA will be explored in hopes of creating more awareness and trigger further work in the related field.","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122268065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Going Big or Going Precise: Considerations in building the next-gen VQA Database","authors":"Franz Götz-Hahn","doi":"10.1145/3423268.3423587","DOIUrl":"https://doi.org/10.1145/3423268.3423587","url":null,"abstract":"Annotated data is a requirement for any kind of modeling of subjective attributes and is usually constrained by a fixed budget available for paying annotators. The distribution of this budget is non-trivial, if the available data is large enough. In the case of video quality assessment (VQA) datasets, it has been commonly deemed more important to evaluate at a higher precision, i.e. getting more annotations for each item, than getting more data annotated less precisely. Considering the highly complex way different technical quality impairments caused by different parts of multiple video processing pipelines interact, the few hundred items comprising existing VQA datasets are unlikely to cover the vast degradation space required to generalize well. An open question, then, is whether some annotation precision can be sacrificed for additional data without loss of generalization power. How does shifting the vote budget from say 1,000 items at 100 annotations to 100,000 items with a single annotation affect predictive performances of state-of-the-art models? This talk addresses this question at the hand of a new large-scale two-part VQA dataset [1] comprising, on the one hand, over 1,500 items annotated with a minimum of 89 votes and, on the other hand, over 150,000 items annotated with 5 votes. Based on this dataset, different VQA approaches were compared at different distributions of a fixed vote budget and, surprisingly, their generalization performance was found to be invariant to this distribution of the budget. This held true for the typical within-dataset testing as well as cross-dataset testing.","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"366 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133848671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Aesthetics and Emotions in Visual Content: From Vincent van Gogh to Robotics and Vision","authors":"J. Z. Wang","doi":"10.1145/3423268.3423586","DOIUrl":"https://doi.org/10.1145/3423268.3423586","url":null,"abstract":"As inborn characteristics, humans possess the ability to judge visual aesthetics, feel the emotions from the environment and comprehend others? emotional expressions. Many exciting applications become possible if robots or computers can be empowered with similar capabilities. Modeling aesthetics, evoked emotions, and emotional expressions automatically in unconstrained situations, however, is daunting due to the lack of a full understanding of the relationship between low-level visual content and high-level aesthetics or emotional expressions. With the growing availability of data, it is possible to tackle these problems using machine learning and statistical modeling approaches. In the talk, I provide an overview of our research in the last two decades on data-driven analyses of visual artworks and digital visual content for modeling aesthetics and emotions. First, I discuss our analyses of styles in visual artworks. Art historians have long observed the highly characteristic brushstroke styles of Vincent van Gogh and have relied on discerning these styles for authenticating and dating his works. In our work, we compared van Gogh with his contemporaries by statistically analyzing a massive set of automatically extracted brushstrokes. A novel extraction method is developed by exploiting an integration of edge detection and clustering-based segmentation. Evidence substantiates that van Gogh's brushstrokes are strongly rhythmic. Next, I describe an effort to model the aesthetic and emotional characteristics in visual contents such as photographs. By taking a data-driven approach, using the Internet as the data source, we show that computers can be trained to recognize various characteristics that are highly relevant to aesthetics and emotions. Future computer systems equipped with such capabilities are expected to help millions of users with unimagined ways. Finally, I highlight our research on automated recognition of bodily expression of emotion. We propose a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize the body language of humans. Comprehensive statistical analysis revealed many interesting insights from the dataset. A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated.","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132598406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Technical to Aesthetics Quality Assessment and Beyond: Challenges and Potential","authors":"Vlad Hosu, D. Saupe, Bastian Goldluecke, Weisi Lin, Wen-Huang Cheng, John See, Lai-Kuan Wong","doi":"10.1145/3423268.3423589","DOIUrl":"https://doi.org/10.1145/3423268.3423589","url":null,"abstract":"Every day 1.8+ billion images are being uploaded to Facebook, Instagram, Flickr, Snapchat, and WhatsApp [6]. The exponential growth of visual media has made quality assessment become increasingly important for various applications, from image acquisition, synthesis, restoration, and enhancement, to image search and retrieval, storage, and recognition. There have been two related but different classes of visual quality assessment techniques: image quality assessment (IQA) and image aesthetics assessment (IAA). As perceptual assessment tasks, subjective IQA and IAA share some common underlying factors that affect user judgments. Moreover, they are similar in methodology (especially NR-IQA in-the-wild and IAA). However, the emphasis for each is different: IQA focuses on low-level defects e.g. processing artefacts, noise, and blur, while IAA puts more emphasis on abstract and higher-level concepts that capture the subjective aesthetics experience, e.g. established photographic rules encompassing lighting, composition, and colors, and personalized factors such as personality, cultural background, age, and emotion. IQA has been studied extensively over the last decades [3, 14, 22]. There are three main types of IQA methods: full-reference (FR), reduced-reference (RR), and no-reference (NR). Among these, NRIQA is the most challenging as it does not depend on reference images or impose strict assumptions on the distortion types and level. NR-IQA techniques can be further divided into those that predict the global image score [1, 2, 10, 17, 26] and patch-based IQA [23, 25], naming a few of the more recent approaches.","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130819044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs","authors":"Kexin Feng, P. Zanwar, A. Behzadan, Theodora Chaspari","doi":"10.1145/3423268.3423584","DOIUrl":"https://doi.org/10.1145/3423268.3423584","url":null,"abstract":"The COVID-19 pandemic caused by the novel SARS-Coronavirus-2 (n-SARS-CoV-2) has impacted people's lives in unprecedented ways. During the time of the pandemic, social vloggers have used social media to actively share their opinions or experiences in quarantine. This paper collected videos from YouTube to track emotional responses in conversational vlogs and their potential associations with events related to the pandemic. In particular, vlogs uploaded from locations in New York City were analyzed given that this was one of the first epicenters of the pandemic in the United States. We observed some common patterns in vloggers' acoustic and linguistic features across the time span of the quarantine, which is indicative of changes in emotional reactivity. Additionally, we investigated fluctuations of acoustic and linguistic patterns in relation to COVID-19 events in the New York area (e.g. the number of daily new cases, number of deaths, and extension of stay-at-home order and state of emergency). Our results indicate that acoustic features, such as zero-crossing-rate, jitter, and shimmer, can be valuable for analyzing emotional reactivity in social media videos. Our findings further indicate that some of the peaks of the acoustic and linguistic indices align with COVID-19 events, such as the peak in the number of deaths and emergency declaration.","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122231569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","authors":"Anonymous","doi":"10.1145/3423268","DOIUrl":"https://doi.org/10.1145/3423268","url":null,"abstract":"The proceedings contain 4 papers The topics discussed include: EVA: an explainable visual aesthetics dataset;from technical to aesthetics quality assessment and beyond: challenges and potential;understanding gender stereotypes and electoral success from visual self-presentations of politicians in social media;and exploring speech cues in web-mined COVID-19 conversational Vlogs","PeriodicalId":393702,"journal":{"name":"Joint Workshop on Aesthetic and Technical Quality Assessment of Multimedia and Media Analytics for Societal Trends","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133523030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}