Prompting Scientific Names for Zero-Shot Species Recognition
This addresses the challenge of recognizing specialized species concepts in zero-shot settings for applications in ecology and biology, offering a simple yet effective method with significant performance gains.
The paper tackled the problem of zero-shot species recognition using Vision-Language Models (VLMs) like CLIP, which perform poorly with scientific names due to their absence in training data, and found that translating scientific names to common English names in prompts improves accuracy by 2 to 5 times on fine-grained species datasets.
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.