Meta-Cobi: A Metadata-Augmented Vision-Language Framework for Multi-view Canine Body Condition Scoring

With the proliferation of obesogenic environments for companion dogs, the objective measurement of the Body Condition Score (BCS) has become increasingly critical as a key health indicator. To overcome the limitations of conventional Convolutional Neural Network (CNN)-based unimodal approaches, this study proposes Meta-Cobi, a Vision-Language Model (VLM)-based framework that integrates multi-view visual data with biological metadata. The framework incorporates the Convolutional Block Attention Module (CBAM) to maximize the density of captured anatomical features and utilizes a Learned Query Pooling mechanism to globally compress information gathered from 11 distinct viewpoints. Furthermore, biological metadata such as breed and age is integrated into structured prompts, while a task-specific special token strategy is employed to enhance inference precision. Experimental results demonstrate that Meta-Cobi achieves superior Macro-F1 score and robustness compared to baseline models, notably maintaining high discriminatory performance even in data-sparse classes. This research expands the scope of AI-driven BCS assessment into the veterinary domain and contributes to the establishment of data-driven precision healthcare systems for companion animals.