Learn About Our Meetup

5000+ Members



Join our meetup, learn, connect, share, and get to know your Toronto AI community. 



Browse through the latest deep learning, ai, machine learning postings from Indeed for the GTA.



Are you looking to sponsor space, be a speaker, or volunteer, feel free to give us a shout.

[D] Why isn’t there more research papers related to active learning for deep computer vision problems?

So perhaps this is a misguided question but to my (limited) understanding the biggest hurdle to applying deep computer vision models from research(ex: classification or object detection) to a new problem is data collection. If you have access to a video stream the problem is: what are the best images to annotate. To me this sounds like it would fall under the Active Learning umbrella, however, I’ve seen a very limited set of papers[1,2,3,4] applied to this. The experiments on these papers also aren’t that great because they don’t reflect reality (images in a video are not i.i.d.)

Am I missing something? Perhaps a better way of selecting what images to annotate that’s not related to active learning?

[1] Deep Active Learning for Object Detection

[2] Cost-Effective Active Learning for Deep Image Classification

[3] The power of ensembles for active learning in image classification

[4] Reducing Class Imbalance during Active Learning for Named Entity Annotation

submitted by /u/CartPole
[link] [comments]

Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.