Enhancing Vision-Language Models with Morphological and Taxonomic Knowledge: Towards Coral Recognition for Ocean Health

Abstract

Coral reefs play a crucial role in marine ecosystems, offering a nutrient-rich environment and safe shelter for numerous marine species. Automated coral image recognition aids in monitoring ocean health at a scale without experts' manual effort. Recently, large vision-language models like CLIP have greatly enhanced zero-shot and low-shot classification capabilities for various visual tasks. However, these models struggle with fine-grained coral-related tasks due to a lack of specific knowledge. To bridge this gap, we compile a fine-grained coral image dataset consisting of 16,659 images with taxonomy labels (from Kingdom to Species), accompanied by morphology-specific text descriptions for each species. Based on the dataset, we propose CORAL-Adapter, integrating two complementary kinds of coral-specific knowledge (biological taxonomy and coral morphology) with general knowledge learned by CLIP. CORAL-Adapter is a simple yet powerful extension of CLIP with only a few parameter updates and can be used as a plug-and-play module with various CLIP-based methods. We show improvements in accuracy across diverse coral recognition tasks, e.g., recognizing corals unseen during training that are prone to bleaching or originate from different oceans.