Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TIGEr: Text-to-Image Grounding for Image Caption Evaluation   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.

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The categories of this paper are: chao-dyn
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat