Timestamp Estimation From Outdoor Scenes

The increasing availability of smartphones allowed people to easily capture and share images on the internet. These images are often associated with metadata, including the image capture time (timestamp) and the location where the image was captured (geolocation). The metadata associated with images provides valuable information to better understand scenes and events presented in these images. The timestamp can be manipulated intentionally to provide false information to convey a twisted version of reality. Images with manipulated timestamps are often used as a cover-up for wrongdoing or broadcasting false claims and competing views on the internet. Estimating the time of capture of a photograph is a challenging task that requires a comprehensive understanding of the scene and its geographical location. In this paper, we propose a learning-based approach based on deep learning to estimate when an outdoor image was captured. We provide a detailed quantitative and qualitative evaluation of the trained models for various settings and show that the proposed approach outperforms baseline methods

Scripted Stereotypes In Reality TV

Diversity, or lack thereof, has always been an issue in both television and film for years. But another great issue that ties in with the lack of diversity is misrepresentation, or a substantial presence of stereotypes in media. While stereotypes often are commonplace in scripted television and film, the possibility of stereotypes appearing in a program that claims to be based on reality seems unfitting. It is commonly known that reality television is not completely “unscripted” and is actually molded by producers and editors. While reality television should not consist of stereotypes, they have curiously made their way onto the screen and into our homes. Through content analysis this thesis focuses on Latina/Hispanic-American and Asian-American contestants on ABCs’ The Bachelor and whether they present stereotypes typically found in scripted programming

Blind Dates: Examining the Expression of Temporality in Historical Photographs

This paper explores the capacity of computer vision models to discern temporal information in visual content, focusing specifically on historical photographs. We investigate the dating of images using OpenCLIP, an open-source implementation of CLIP, a multi-modal language and vision model. Our experiment consists of three steps: zero-shot classification, fine-tuning, and analysis of visual content. We use the \textit{De Boer Scene Detection} dataset, containing 39,866 gray-scale historical press photographs from 1950 to 1999. The results show that zero-shot classification is relatively ineffective for image dating, with a bias towards predicting dates in the past. Fine-tuning OpenCLIP with a logistic classifier improves performance and eliminates the bias. Additionally, our analysis reveals that images featuring buses, cars, cats, dogs, and people are more accurately dated, suggesting the presence of temporal markers. The study highlights the potential of machine learning models like OpenCLIP in dating images and emphasizes the importance of fine-tuning for accurate temporal analysis. Future research should explore the application of these findings to color photographs and diverse datasets

Native advertising : attitudes, value and purchase intention

textNative-form advertising in the digital space can most easily be defined as promotional content constructed to mimic the form and structure of the website that it is embedded on. With the rise of user generated content and social media, digital native advertising is fast becoming a popular promotional tactic for brands looking to engage with an online audience. This study examines whether this form of advertising significantly impacts consumer attitudes towards the ad, value of the ad and purchase intention of the promoted product across three product categories. Although not significant, results suggest that native advertising positively impacts entertainment- and lifestyle-based products, while information-based service industries, including cyber security, saw a negative reaction from respondents. That said, product category did influence attitude toward the ad and ad value regardless of the ad type. Moreover, a strong positive correlation between product involvement and purchase intention was found, indicating the need to target specific audiences with online native advertising.Advertisin

Trust Needs Touch: Understanding the Building of Trust through Social Presence

Trust is gaining in importance in today’s digital world where interactions become more and more impersonal. In this context, many studies show that social presence, i.e. the feeling of human contact, has a positive effect on the formation of trust. However, the theoretical explanation of the relationship is still somewhat unexplored in the IS domain. In this study, we draw on psychology literature and derive a comprehensive framework to conceptualize and explain the relationship in detail. Particularly, we identify four mechanisms that were not yet explicated by IS research. Using the developed framework as a structuring device, we then carry out a structured literature review in the IS domain to identify existing studies and their theoretical focus as well as to point out research gaps. We are able to show that there is much more to the relationship between social presence and trust than the IS domain has yet recognized

Learning to Map the Visual and Auditory World

The appearance of the world varies dramatically not only from place to place but also from hour to hour and month to month. Billions of images that capture this complex relationship are uploaded to social-media websites every day and often are associated with precise time and location metadata. This rich source of data can be beneficial to improve our understanding of the globe. In this work, we propose a general framework that uses these publicly available images for constructing dense maps of different ground-level attributes from overhead imagery. In particular, we use well-defined probabilistic models and a weakly-supervised, multi-task training strategy to provide an estimate of the expected visual and auditory ground-level attributes consisting of the type of scenes, objects, and sounds a person can experience at a location. Through a large-scale evaluation on real data, we show that our learned models can be used for applications including mapping, image localization, image retrieval, and metadata verification

Deep Probabilistic Models for Camera Geo-Calibration

The ultimate goal of image understanding is to transfer visual images into numerical or symbolic descriptions of the scene that are helpful for decision making. Knowing when, where, and in which direction a picture was taken, the task of geo-calibration makes it possible to use imagery to understand the world and how it changes in time. Current models for geo-calibration are mostly deterministic, which in many cases fails to model the inherent uncertainties when the image content is ambiguous. Furthermore, without a proper modeling of the uncertainty, subsequent processing can yield overly confident predictions. To address these limitations, we propose a probabilistic model for camera geo-calibration using deep neural networks. While our primary contribution is geo-calibration, we also show that learning to geo-calibrate a camera allows us to implicitly learn to understand the content of the scene