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