Show the same photo of a cat to two different neural networks, and they often reach a similar conclusion: "cat." But ask them for more detail, and their answers may diverge. One model focuses on the whiskers, another on the coat color, a third on the pose. So even within the "visual" world of neural networks, there's no complete unity.

Researchers devised a way to measure this unity for each image individually. They took several visual neural networks with different training tasks and compared their internal representations. For some images, the models are nearly unanimous; for others, they diverge significantly. Then they showed these images to language models that describe images in words. It turned out: when visual models agree with each other, their collective opinion aligns better with the language description. The difference can be twofold.

This result suggests that agreement among neural networks is not a coincidence. It reflects how "understandable" the image turned out to be. The clearer its structure, the easier it is for both vision and language to find common ground. This approach helps reveal where neural networks converge with human perception and where they diverge.