cyens Goto Github PK

ccp-configurable-crowd-profiles

[SIGGRAPH 2022] CCP: Configurable Crowd Profiles

cfd-backgrounds

continual_learning_on_the_edge_with_tensorflow-lite

reinherit-hadjigeorgakis-kornesios-mansion

reinheritarapp

The Augmented Reality (AR) application of the ReInHerit project

reinheritperformanceboccf

robust-artwork-recognition-using-smartphone-cameras

A cross-platform smartphone app for the Cypriot National Gallery of Modern Art.

social-beyeas-dataset

Image analysis algorithms have become an indispensable tool in our information ecosystem, facilitating new forms of visual communication and information sharing. At the same time, they enable large-scale socio-technical research which would otherwise be difficult to carry out. However, their outputs may exhibit social bias, especially when analyzing people images. Since most algorithms are proprietary and opaque, we pro-pose a method of auditing their outputs for social biases. To be able to compare how algorithms interpret a controlled set of people images, we collected descriptions across six image tagging APIs. In order to com-pare these results to human behavior, we also collected descriptions on the same images from crowdworkers in two anglophone regions. While the APIs do not output explicitly offensive descriptions, as humans do, future work should consider if and how they reinforce social inequalities in implicit ways. Beyond computer vision auditing, the dataset of human- and machine-produced tags, and the typology of tags, can be used to explore a range of research questions related to both algorithmic and human behaviors. (2019-01-15)

social-beyeas-dataset-v2.0

Researchers of Web and social media rely extensively on image analysis tools to understand users' sharing behaviors and engagement with content on the large scale. However, it has been made clear over the past years that there are disparities in the way that these tools treat images depicting people from different social groups. Previously, we released the Social B(eye)as Dataset, consisting of machine- and human-generated descriptions on a controlled set of people images without context. This resource allows researchers to compare the behaviors of taggers and humans systematically. We now update this, with a process that imposes the people-images onto backgrounds. The current release uses four stereotypically "feminine" and four "masculine" contexts. Thus, it enables us to consider the possible influences upon the gender inferences that are made by tagging algorithms. We also provide an updated typology of tags used by the six proprietary taggers as well as initial analyses. Our methodology for imposing semi-transparent images onto background images is publicly available, allowing others to repeat the process with other combinations of images for various research topics. (2020-01-15)

trafficflow

Reinforcement learning experiments on traffic flow control and optimisation