  Evaluation metrics for image captioning face two challenges. Firstly,
commonly used metrics such as CIDEr, METEOR, ROUGE and BLEU often do not
correlate well with human judgments. Secondly, each metric has well known blind
spots to pathological caption constructions, and rule-based metrics lack
provisions to repair such blind spots once identified. For example, the newly
proposed SPICE correlates well with human judgments, but fails to capture the
syntactic structure of a sentence. To address these two challenges, we propose
a novel learning based discriminative evaluation metric that is directly
trained to distinguish between human and machine-generated captions. In
addition, we further propose a data augmentation scheme to explicitly
incorporate pathological transformations as negative examples during training.
The proposed metric is evaluated with three kinds of robustness tests and its
correlation with human judgments. Extensive experiments show that the proposed
data augmentation scheme not only makes our metric more robust toward several
pathological transformations, but also improves its correlation with human
judgments. Our metric outperforms other metrics on both caption level human
correlation in Flickr 8k and system level human correlation in COCO. The
proposed approach could be served as a learning based evaluation metric that is
complementary to existing rule-based metrics.
