SPECS: Specificity-Enhanced CLIP-Score for Long Image Caption Evaluation
This provides a practical, efficient evaluation tool for researchers and developers working on long image captioning models, though it is incremental as it builds on existing CLIP-based approaches.
The paper tackles the problem of unreliable evaluation metrics for long, detailed image captions by introducing SPECS, a reference-free metric that matches the performance of LLM-based metrics in correlation to human judgments while being far more efficient.
As interest grows in generating long, detailed image captions, standard evaluation metrics become increasingly unreliable. N-gram-based metrics though efficient, fail to capture semantic correctness. Representational Similarity (RS) metrics, designed to address this, initially saw limited use due to high computational costs, while today, despite advances in hardware, they remain unpopular due to low correlation to human judgments. Meanwhile, metrics based on large language models (LLMs) show strong correlation with human judgments, but remain too expensive for iterative use during model development. We introduce SPECS (Specificity-Enhanced CLIPScore), a reference-free RS metric tailored to long image captioning. SPECS modifies CLIP with a new objective that emphasizes specificity: rewarding correct details and penalizing incorrect ones. We show that SPECS matches the performance of open-source LLM-based metrics in correlation to human judgments, while being far more efficient. This makes it a practical alternative for iterative checkpoint evaluation during image captioning model development.Our code can be found at https://github.com/mbzuai-nlp/SPECS.