Title: $\mathrm{R}^2$-VOS: Robust Referring Video Object Segmentation via Relational Cycle Consistency. Abstract: Referring video object segmentation (R-VOS) aims to segment the object masks in a video given a referring linguistic expression to the object. R-VOS introduces human language in the traditional VOS loop to extend flexibility, while all current studies are based on a strict assumption: the object depicted by the expression must exist in the video, namely, the expression and video must have an object-level semantic consensus. This is often violated in real-world applications where an expression can be queried to false videos, and existing methods always fail due to abusing the assumption. In this work, we emphasize that studying semantic consensus is necessary to improve the robustness of R-VOS. Accordingly, we pose an extended task from R-VOS without the semantic consensus assumption, named Robust R-VOS ($\mathrm{R}^2$-VOS). The new task essentially corresponds to the joint modeling of the primary R-VOS problem and its dual (text reconstruction). We embrace the observation that the textual embedding spaces have relational structure consistency in the text-video-text transformation cycle that links the primary and dual problems. We leverage the cycle consistency to consolidate and discriminate the semantic consensus, thus advancing the primary task. We then propose an early grounding module to enable the parallel optimization of the primary and dual problems. To measure the robustness of R-VOS models against unpaired videos and expressions, we construct a new evaluation dataset, $\mathrm{R}^2$-Youtube-VOS. Extensive experiments demonstrate that our method not only identifies negative text-video pairs but also improves the segmentation accuracy for positive pairs with superior disambiguating ability. Our model achieves the state-of-the-art performance on Ref-DAVIS17, Ref-Youtube-VOS, and $\mathrm{R}^2$-Youtube-VOS dataset.