<ul><li>Vision Transformers (ViTs) are powerful in computer vision tasks due to their representation capabilities.</li><li>A layer-wise analysis of ViTs using neuron labeling reveals that concepts encoded in ViTs become more complex throughout the network.</li><li>Early layers primarily encode basic features like colors and textures, while later layers represent more specific classes, such as objects and animals.</li><li>Different pretraining strategies influence the quantity and category of encoded concepts, with finetuning reducing the number of concepts and shifting them to more relevant categories.</li></ul>

From Colors to Classes: Emergence of Concepts in Vision Transformers

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