SourceA Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
The authors evaluated ProtoPFormer's ability to identify fine-grained visual attributes by showing its prototype activation maps to seven human raters unfamiliar with the work. They randomly selected five images from each of four CUB species (20 images total) and asked raters to list attributes captured by the maps. An attribute was counted as detected if more than half of the raters identified it. ProtoPFormer achieved 42.2% attribute identification accuracy, compared to 74.7% for the authors' INTR model on the same protocol.