Which learning tasks do brains use to train themselves to see? In computer vision is very common to use supervised tasks, where datasets have to be manually annotated by humans. Some examples are object classification (class labels), detection (bounding boxes) and segmentation (pixel-level masks). These datasets are essentially pairs of inputs-outputs which are used to train Convolutional Neural Networks to learn the mapping from inputs to outputs, via gradient descent optimization. But animals don't need anybody to show them bounding boxes or masks on top of things in order for them to learn to detect objects and make sense of the visual world around them. This leads me to think that brains must be performing some sort of self-supervision to train themselves to see.

What does current research say about the learning paradigm used by brains to achieve such an outstanding level of visual competence? Which tasks do brains use to train themselves to be so good at processing visual information and making sense of the visual world around them? Or said in other words: how does the brain manage to train its neural networks without having access to manually annotated datasets like ImageNet, COCO, etc. (i.e. how does the brain manage to generate its own training examples)? Finally, can we apply these insights in computer vision?
Maybe this paper will give you an overview about (and entrance to) this topic from biological side. It is a review about the state of the art in human brain development (and its implications for clinical treatment).

The table of contents include for example


Stage 1: the first year, early maturation of vision and the structure of V1 neurobiology
Stage 2: preschool children have high variability in V1 development (1–4 years)
Stage 3: experience-dependent visual development in school aged children (5–11 years)


(V1 means "visual cortex")
Where this points all handle 3 categories: visual milestones (i.e. contrast sensitivity, contour integration), anatomical milestones (i.e. morphology) and neurobiological milestones (i.e. synapsis, but also a lot of genetics).

And as second : Maybe you could ask this question on bioinformatics.SE too, because this connection between biological example and computational reproduction is one of their fields.