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Image captioning models have been able to generate grammatically correct andhuman understandable sentences. However most of the captions convey limitedinformation as the model used is trained on datasets that do not caption allpossible objects existing in everyday life. Due to this lack of priorinformation most of the captions are biased to only a few objects present inthe scene, hence limiting their usage in daily life. In this paper, we attemptto show the biased nature of the currently existing image captioning models andpresent a new image captioning dataset, Egoshots, consisting of 978 real lifeimages with no captions. We further exploit the state of the art pre-trainedimage captioning and object recognition networks to annotate our images andshow the limitations of existing works. Furthermore, in order to evaluate thequality of the generated captions, we propose a new image captioning metric,object based Semantic Fidelity (SF). Existing image captioning metrics canevaluate a caption only in the presence of their corresponding annotations;however, SF allows evaluating captions generated for images withoutannotations, making it highly useful for real life generated captions.

Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning models