I am given 2 sets of images. All the images in both sets are without annotations and labels.
First set : a set of images of the grocery store shelves (captured in the grocery stores).
Second set: a set of close-up images of the products kept on those store shelves.
What I am trying to achieve:
Goal: For every product image, I want to find the location of that product in all shelf images in which it appears.
- For each product image, first find all the shelf image(s) which contain that product.
- Then predict a bounding box by finding the location of the product in the shelf image.
I am using YOLOv5 for this task but I am not sure how should I start off given that I have to do it without annotations or labels.
I have come across terms like Zero-shot learning, self-supervised Object Detection, etc. but I haven't been able to figure out their use case as a starting point.
There's a similar question asked on StackOverflow but I am not sure the answer to it solves the problem.