  Trained on web-scale image-text pairs, Vision-Language Models (VLMs) such as
CLIP can recognize images of common objects in a zero-shot fashion. However, it
is underexplored how to use CLIP for zero-shot recognition of highly
specialized concepts, e.g., species of birds, plants, and animals, for which
their scientific names are written in Latin or Greek. Indeed, CLIP performs
poorly for zero-shot species recognition with prompts that use scientific
names, e.g., "a photo of Lepus Timidus" (which is a scientific name in Latin).
Because these names are usually not included in CLIP's training set. To improve
performance, prior works propose to use large-language models (LLMs) to
generate descriptions (e.g., of species color and shape) and additionally use
them in prompts. We find that they bring only marginal gains. Differently, we
are motivated to translate scientific names (e.g., Lepus Timidus) to common
English names (e.g., mountain hare) and use such in the prompts. We find that
common names are more likely to be included in CLIP's training set, and
prompting them achieves 2$\sim$5 times higher accuracy on benchmarking datasets
of fine-grained species recognition.
