ArtClass v2 utilizes a U-Net backbone operating in a latent space of $8 \times 8$ downsampling. The core innovation lies in the fine-tuning of the attention layers on the ArtClass-Corpus.

ArtClass v2 offers a more realistic, multi-label, and challenging benchmark for artwork FGVC. Our experiments show that current models struggle with hard pairs and multi-label style attribution. Future work includes:

A blind study with 200 participants (mix of artists and laypeople) asked subjects to choose which image best represented a given prompt.

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