Most state-of-the-art semi-supervised video object segmentation methods rely on a pixel-accurate mask of a target, object provided for the first frame of a video. However, obtaining a detailed segmentation mask is expensive and time-consuming. In this work we explore an alternative way of identifying a target object, namely by employing language referring expressions. Besides being a more practical and natural way of pointing out a target object, using language specifications can help to avoid drift as well as make the system more robust to complex dynamics and appearance variations. Leveraging recent advances of language grounding models designed for images, we propose an approach to extend them to video data, ensuring temporally coherent predictions. To evaluate our approach we augment the popular video object, segmentation benchmarks, DAVIS_(16) and DAVIS_(17) with language descriptions of target objects. We show that our language-supervised approach performs on par with the methods which have access to a pixel-level mask of the target object on DAVIS_(16) and is competitive to methods using scribbles on the challenging DAVIS_(17) dataset.
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