HomLLM: Exploiting Semantic Homology Relationship for Fine-Grained Bird Image Classification via Large Language...

Area of Science:

• Computer Vision
• Machine Learning
• Computational Biology

Background:

• Fine-grained bird image classification (FBIC) faces challenges like varied postures, similar species, and occlusions.
• Accurate recognition of endangered bird species is crucial for conservation efforts.

Purpose of the Study:

• To develop a novel method, HomLLM, for effective fine-grained bird classification using large language models (LLMs).
• To address limitations in FBIC by learning semantic homology relationships.

Main Methods:

• Proposed HomLLM model leveraging semantic homology relationship representation learning.
• Introduced Semantic Homology Generation (SHG) and Homology Relationship Mining (HRM) modules.
• Utilized LLMs for multi-granularity feature description and hierarchical cross-modal interaction.

Main Results:

• Identified invariant homology in key bird regions across different postures.
• Established homological relationships as essential taxonomic markers for similar bird classes.
• HomLLM demonstrated superior performance compared to state-of-the-art methods on CUB-200-2011 and NABirds datasets.

Conclusions:

• The proposed HomLLM effectively addresses FBIC challenges by learning invariant structural correspondences.
• Semantic homology representations derived from LLMs enhance the discriminability of bird species.
• This approach offers a promising direction for automated species identification and conservation.