8,904 research outputs found
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
The unprecedented increase in the usage of computer vision technology in
society goes hand in hand with an increased concern in data privacy. In many
real-world scenarios like people tracking or action recognition, it is
important to be able to process the data while taking careful consideration in
protecting people's identity. We propose and develop CIAGAN, a model for image
and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies
while producing high-quality images and videos that can be used for any
computer vision task, such as detection or tracking. Unlike previous methods,
we have full control over the de-identification (anonymization) procedure,
ensuring both anonymization as well as diversity. We compare our method to
several baselines and achieve state-of-the-art results.Comment: CVPR 202
LoGAN: Generating Logos with a Generative Adversarial Neural Network Conditioned on color
Designing a logo is a long, complicated, and expensive process for any
designer. However, recent advancements in generative algorithms provide models
that could offer a possible solution. Logos are multi-modal, have very few
categorical properties, and do not have a continuous latent space. Yet,
conditional generative adversarial networks can be used to generate logos that
could help designers in their creative process. We propose LoGAN: an improved
auxiliary classifier Wasserstein generative adversarial neural network (with
gradient penalty) that is able to generate logos conditioned on twelve
different colors. In 768 generated instances (12 classes and 64 logos per
class), when looking at the most prominent color, the conditional generation
part of the model has an overall precision and recall of 0.8 and 0.7
respectively. LoGAN's results offer a first glance at how artificial
intelligence can be used to assist designers in their creative process and open
promising future directions, such as including more descriptive labels which
will provide a more exhaustive and easy-to-use system.Comment: 6 page, ICMLA1
Beyond deep fakes: Conceptual framework, applications, and research agenda for neural rendering of realistic digital faces
Neural rendering (NR) has emerged as a novel technology for the generation and animation of realistic digital human faces. NR is based on machine learning techniques such as generative adversarial networks and is used to infer human face features and their animation from large amounts of (video) training data. NR shot to prominence with the deep fake phenomenon, the malicious and unwanted use of someoneâs face for deception or satire. In this paper we demonstrate that the potential uses of NR far outstrip its use for deep fakes. We contrast NR approaches with traditional computer graphics approaches, discuss typical types of NR applications in digital face generation, and derive a conceptual framework for both guiding the design of digital characters, and for classifying existing NR use cases. We conclude with research ideas for studying the potential applications and implications of NR-based digital characters
Exploring TV Seriality and Television Studies through Data-Driven Approaches
The chapter discusses the use of data-driven approaches in television studies, which has become possible due to the increasing availability of digital data. Computational techniques can be used to analyze cultural artifacts, gain insights into audience reactions to specific shows or episodes, and investigate patterns and trends in television programming over time. The chapter also highlights the challenges of analyzing television series, which are complex open systems that interact with external factors such as the production process, audience feedback, and cultural and social context. Content analysis, which involves qualitative and quantitative methods based on the coding process and data collection, can be used to analyze various elements of a TV series.
Generative AI is also discussed, which refers to the use of deep multi-modal algorithms to generate new content such as images, speech, and text. Generative methods like Generative Adversarial Networks (GANs) and Stable Diffusion can create new content that is almost indistinguishable from real data. While generating videos is more challenging, Recurrent Neural Networks (RNNs) like LSTMs can capture the temporal dynamics of the scenes to create interesting and promising applications for complex, but short-duration videos
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