yeah having that convolution prior is definitely useful when you're dealing with limited amount of data, because you're encoding problem structure into the mode

yeah having that convolution prior is definitely useful when you're dealing with limited amount of data, because you're encoding problem structure into the model, which is why they get away with being trained on fewer samples but with a trade off around generalization. but i think this moment is quite different because instead of baking everything in the latent space for these models, you're letting them reason how a human would - if i was asked to detect for the street sign i'd first start by zooming into different regions and iteratively figure out what is relevant. Yolo and other models don't do this well enough because they lack the language component which is a must have for complex reasoning like this for example: https://www.spatial-reasoning.com/share/2d4a8827-b227-4f23-a89b-3687bc931948 https://www.spatial-reasoning.com/share/2d4a8827-b227-4f23-a.... Like 4o can't do this even though it most likely has the same vision encoder as o4. this is the power of reasoning.