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Image captioning models are usually trained according to human annotatedground-truth captions, which could generate accurate but generic captions. Inthis paper, we focus on generating distinctive captions that can distinguishthe target image from other similar images. To evaluate the distinctiveness ofcaptions, we introduce a series of metrics that use large-scale vision-languagepre-training model CLIP to quantify the distinctiveness. To further improve thedistinctiveness of captioning models, we propose a simple and effectivetraining strategy that trains the model by comparing target image with similarimage group and optimizing the group embedding gap. Extensive experiments areconducted on various baseline models to demonstrate the wide applicability ofour strategy and the consistency of metric results with human evaluation. Bycomparing the performance of our best model with existing state-of-the-artmodels, we claim that our model achieves new state-of-the-art towardsdistinctiveness objective.

Distinctive Image Captioning via CLIP Guided Group Optimization