Fantastic. The ability to train without labourious labelling/annotations can really help produce effective models appropriate for real-world use cases. No code

Fantastic. The ability to train without labourious labelling/annotations can really help produce effective models appropriate for real-world use cases. No code as yet, but looking forward to having it released and playing with it: https://github.com/charigyang/motiongrouping https://github.com/charigyang/motiongrouping *Edit: It will be interesting to see if this works Video > Image as well. Much of the current video AI work stems from the image foundation, ie split a video into constituent frames and run detection models on those images. But the image detection/segmentation models assume each image is different, and so the process when parsing video this way is unnecessarily complex - sequential video frames in the same scene are more alike than different. If good segmentation models for video can be more easily trained using this method, then it would be interesting if they can also be applied accurately to still images, since a snapshot of a video is a single image anyway.