Computer Vision should take into account fraction identified to species

[upupa-epops]

This sounds related to this common issue (my comment on New Computer Vision Model (v2.17) with over 1,000 new species!):

this is a relatively common situation with plants and invertebrates as well, where some species in a genus (or larger group) can be identified while other species are never possible to identify. The CV recognizes the impossible species as being similar to the possible species, and recommends the wrong species because it doesn’t know about the existence of the impossible species. There are lists on the forum (1, 2) of species for which identifiers need to constantly keep up with these erroneous CV identifications.

It can help a lot if there are multiple possible but very similar species that the CV knows (let’s say a genus of 5 species, where 2 are possible to ID but challenging, while the other 3 are impossible to ID), because then it gets less confident between them and goes back to genus to be safe.

I think there are probably ways that the CV could be optimized to reduce this, but I feel like it would be pretty complicated and take a bunch of problem-solving to figure out how to do it well.

The main question I think is how do you make it aware that other similar species exist, if there aren’t enough observations of them for those species to be added to the training pool? If it isn’t aware of the species then it doesn’t know whether they look identical to the species it knows vs. existing but being very distinct, or even whether they exist at all.