TIGEr: Text-to-Image Grounding for Image Caption Evaluation
This addresses the need for more reliable evaluation metrics in image captioning research, though it is incremental as it builds upon existing text-image grounding models.
The paper tackles the problem of biased automatic evaluation in image captioning by introducing TIGEr, a new metric that uses text-image grounding to assess caption quality based on image content and human captions, showing higher consistency with human judgments than existing metrics.
This paper presents a new metric called TIGEr for the automatic evaluation of image captioning systems. Popular metrics, such as BLEU and CIDEr, are based solely on text matching between reference captions and machine-generated captions, potentially leading to biased evaluations because references may not fully cover the image content and natural language is inherently ambiguous. Building upon a machine-learned text-image grounding model, TIGEr allows to evaluate caption quality not only based on how well a caption represents image content, but also on how well machine-generated captions match human-generated captions. Our empirical tests show that TIGEr has a higher consistency with human judgments than alternative existing metrics. We also comprehensively assess the metric's effectiveness in caption evaluation by measuring the correlation between human judgments and metric scores.