annotation
Images are the fuel of computer vision. In this text, the sociologists and computer science researchers Miceli Milagros and Tianling Yang, discuss the performativity of ground truth data that are produced in image labeling processes.
TPG
2023-07
A deep dive into annotation for machine learning, with an increasing focus on emotional content and contexts
Artificial intelligence may make some jobs obsolete but it has given a new life to a group of people who play an unglamorous but critical role in machine learning: first generation women workers in …
TPG
2020-10
“Question to twitterverse: A lovely PhD student and I are looking for papers and other projects from the social sciences and humanities on computer vision, image recognition and on the production of …
TPG
2020-09
Javier Lloret Pardo - Annotators View Image Annotators constitute the hidden labour of AI vision. The current ubiquitous techniques of image classification, segmentation and scene description …
TPG
2020-09
We propose Localized Narratives, a new form of multimodal image annotations connecting vision and language. We ask annotators to describe an image with their voice while simultaneously hovering their …
I proposed what would become Lacework in the Summer of 2019. In my proposal, I describe a cycle of videos curated from MIT's 'Moments In Time' dataset, each then slowed down, interpolated, and upscaled immensely into imagined detail, one flowing into another like a river...
We propose Localized Narratives, an efficient way to collect image captions with dense visual grounding. We ask annotators to describe an image with their voice while simultaneously hovering their …
The Future Is Here!, the title of Mimi Onuoha’s video project reflecting the human side of crowdsourced image labelling, is spot on. The stories I have been told by crowd workers from across the globe doing this work full-time indeed often have an eerily Gibsonian ring to them. Especially the stories from Venezuela.
A popular self-driving car dataset is missing labels for hundreds of pedestrians https://blog.roboflow.ai/self-driving-car-dataset-missing-pedestrians/
In September 2019 the ImageNet creator Fei-Fei Li gave a talk at The Photographers' Gallery talking through the events and key people that led to the creation of visual datasets.
An introductory presentation about Data / Set / Match, a year-long programme seeking new ways to present, visualise and interrogate contemporary image datasets. Departing from traditional 19th and …
What do you see, YOLO9000? by Taller Estampa | Soy Cámara YOLO9000 is a trained object recognition neuronal network with a dataset of 9,418 words and millions of images. It is one of the many …
We introduce the first visual privacy dataset originating from people who are blind in order to better understand their privacy disclosures and to encourage the development of algorithms that can …
This labelling job has made me very observant. I have found pictures that made me think “if I had taken such a picture, then I would know what is everything.” For instance, in a picture of a …
2017-08
The second part of the interview between Sebastian Schmieg and Nicolas Malevé. Schmieg reflects on his project 'Search by Image' and further discusses machine learning and intelligence as well the politics of image annotation.
Quotations around automation, image making and labour. Collated by Adam Brown and Nicolas Malevé for Rethinking the workshop: Workers Education in the Age of Intelligent Machines at The …
We are under the illusion that seeing is effortless, but fre- quently the visual system is lazy and makes us believe that we understand something when in fact we don’t. Labeling a picture forces us …
TPG
2017-01
“Cropping, montage and annotations... Exploring photographic practices in conspiracy theory. http://t.co/gDA93cW5ev”
Computer Vision: On the Way to Seeing More source: New York Times / imSitu