CLAIR: Evaluating Image Captions with Large Language Models
read the original abstract
The evaluation of machine-generated image captions poses an interesting yet persistent challenge. Effective evaluation measures must consider numerous dimensions of similarity, including semantic relevance, visual structure, object interactions, caption diversity, and specificity. Existing highly-engineered measures attempt to capture specific aspects, but fall short in providing a holistic score that aligns closely with human judgments. Here, we propose CLAIR, a novel method that leverages the zero-shot language modeling capabilities of large language models (LLMs) to evaluate candidate captions. In our evaluations, CLAIR demonstrates a stronger correlation with human judgments of caption quality compared to existing measures. Notably, on Flickr8K-Expert, CLAIR achieves relative correlation improvements over SPICE of 39.6% and over image-augmented methods such as RefCLIP-S of 18.3%. Moreover, CLAIR provides noisily interpretable results by allowing the language model to identify the underlying reasoning behind its assigned score. Code is available at https://davidmchan.github.io/clair/
This paper has not been read by Pith yet.
Forward citations
Cited by 4 Pith papers
-
Do Audio-Visual Large Language Models Really See and Hear?
AVLLMs encode audio semantics in middle layers but suppress them in final text outputs when audio conflicts with vision, due to training that largely inherits from vision-language base models.
-
ClaimDiff-RL: Fine-Grained Caption Reinforcement Learning through Visual Claim Comparison
ClaimDiff-RL replaces holistic scalar rewards with reference-conditioned atomic claim differences verified by a multimodal judge to improve the hallucination-missing-fact tradeoff in long-form image captioning.
-
ClaimDiff-RL: Fine-Grained Caption Reinforcement Learning through Visual Claim Comparison
ClaimDiff-RL introduces reference-conditioned atomic claim differences verified by a multimodal judge as the reward signal for fine-grained RL in long-form image captioning.
-
VC-Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysis
VC-Inspector introduces a lightweight open-source LMM and a controllable factual-error generation framework that achieves state-of-the-art correlation with human judgments on reference-free video caption evaluation.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.