CAPEval: Decoupled Caption Evaluation for Understanding and Generation
Original title:CAPEval: A Decoupled Caption Evaluation across Understanding and Generation
AI Summary
CAPEval proposes evaluating image captions through two separate dimensions: Coverage, which measures how thoroughly a caption captures factual visual content, and Precision, which measures the fraction of its claims that are supported by the image. The benchmark uses human-written ground-truth captions and human-verified atomic checklist items. Experiments compare 10 captioners from four model families under controlled downstream settings where caption source is the only changing variable. The reported results show that Coverage is more strongly associated with multimodal understanding, while Precision is the dominant predictor of text-to-image generation performance.
Why it's worth reading
Caption quality is used for both multimodal understanding and image generation, but the paper argues that these tasks need different optimization targets. CAPEval offers a concrete way to select captioners by downstream use rather than one aggregate score.
Deep Read
What happened
Original facts: The paper introduces CAPEval, or Coverage And Precision Evaluation, which decomposes caption quality into two dimensions instead of one scalar score. It uses human-written ground-truth captions and human-verified atomic checklist items, then evaluates 10 captioners from four model families.
Core tech
Original facts: Coverage measures how thoroughly a caption covers factual visual content. Precision measures the fraction of claims expressed by the caption that are supported by the image. The design separates missing information from unsupported or incorrect information. Analysis: This resembles separating a caption’s factual recall tendency from its claim reliability.
Key evidence & numbers
Original facts: The authors run controlled end-to-end downstream experiments in which caption source is the only changing variable. The experiments include 10 captioners across four model families. The abstract reports a consistent task-dependent dissociation: Coverage is the stronger correlate of understanding performance, while Precision is the dominant predictor of generation performance. It does not provide correlation coefficients, dataset sizes, or confidence intervals.
Why it matters
Analysis: A single caption score can hide opposing requirements across downstream tasks. Multimodal understanding may benefit from more complete visual content, while text-to-image training may be especially sensitive to hallucinated or unsupported claims. Separating the dimensions enables task-specific data and captioner optimization.
Practical impact
Analysis: Teams building multimodal understanding data could prioritize Coverage, while teams preparing text-to-image captions could prioritize Precision. CAPEval may also support captioner comparison, training-example filtering, and diagnosis of omission versus factual-error failures. Unverified inference: Whether this directly improves production training depends on annotation cost, atomic-claim quality, and compatibility with particular model architectures.
Limitations & uncertainty
The abstract does not specify the image sources, domain coverage, languages, checklist construction process, or evaluator agreement. Coverage depends on reference captions and may miss valid image facts that references omit. Precision may also vary with the granularity used to split claims into atomic units. The reported “stronger correlation” and “dominant predictor” should be checked against the full paper’s statistical methodology and cross-dataset validation.
Original sources
- arXiv:2608.02589
- Source: Hugging Face Papers, based on the paper entry supplied by the user