  Automatic image captioning evaluation is critical for benchmarking and
promoting advances in image captioning research. Existing metrics only provide
a single score to measure caption qualities, which are less explainable and
informative. Instead, we humans can easily identify the problems of captions in
details, e.g., which words are inaccurate and which salient objects are not
described, and then rate the caption quality. To support such informative
feedback, we propose an Informative Metric for Reference-free Image Caption
evaluation (InfoMetIC). Given an image and a caption, InfoMetIC is able to
report incorrect words and unmentioned image regions at fine-grained level, and
also provide a text precision score, a vision recall score and an overall
quality score at coarse-grained level. The coarse-grained score of InfoMetIC
achieves significantly better correlation with human judgements than existing
metrics on multiple benchmarks. We also construct a token-level evaluation
dataset and demonstrate the effectiveness of InfoMetIC in fine-grained
evaluation. Our code and datasets are publicly available at
https://github.com/HAWLYQ/InfoMetIC.
