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Learning Visual-Linguistic Adequacy, Fidelity, and Fluency for Novel Object Captioning

Novel object captioning (NOC) learns image captioning models for describing objects or visual concepts which are unseen (i.e., novel) in the training captions. Such captioning models need to sufficiently describe such visual data with fluent and natural language expression. In other words, we expect the produced captions being linguistically fluent, containing novel objects of interest, and fitting the visual concept of the image. The above three aspects thus correspond to fluency, fidelity, and adequacy, respectively. However, most novel object captioning models are not explicitly designed to address the aforementioned properties due to the absence of caption annotations. In this paper, we start by providing an insight into the relationship between the above properties and existing visual/language models. Then, we present VLAF2, for learning Visual-Linguistic Adequacy, Fidelity, and Fluency, which utilizes linguistics observed from captions for describing visual information of images with novel objects. More specifically, we revisit BERT and CLIP, and explain how we leverage the intrinsic language knowledge from such popular models to reward captions with precise and rich visual content associated with novel images. To validate the effectiveness of our framework, we conduct extensive experiments on the nocaps dataset. Our method not only performs favorably against state-of-the-art novel captioning models in all caption evaluation metrics, but also surpasses the SPICE scores of human baseline. We perform quantitative and qualitative analysis to demonstrate how our model generates novel object captions with improved fluency, fidelity, and adequacy. Implementation details and code are available in the supplementary materials.

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