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Referring video object segmentation (RVOS) aims at segmenting an object in avideo following human instruction. Current state-of-the-art methods fall intoan offline pattern, in which each clip independently interacts with textembedding for cross-modal understanding. They usually present that the offlinepattern is necessary for RVOS, yet model limited temporal association withineach clip. In this work, we break up the previous offline belief and propose asimple yet effective online model using explicit query propagation, namedOnlineRefer. Specifically, our approach leverages target cues that gathersemantic information and position prior to improve the accuracy and ease ofreferring predictions for the current frame. Furthermore, we generalize ouronline model into a semi-online framework to be compatible with video-basedbackbones. To show the effectiveness of our method, we evaluate it on fourbenchmarks, \ie, Refer-Youtube-VOS, Refer-DAVIS17, A2D-Sentences, andJHMDB-Sentences. Without bells and whistles, our OnlineRefer with a Swin-Lbackbone achieves 63.5 J&F and 64.8 J&F on Refer-Youtube-VOS and Refer-DAVIS17,outperforming all other offline methods.

OnlineRefer: A Simple Online Baseline for Referring Video Object Segmentation