  Referring video object segmentation (R-VOS) is an emerging cross-modal task
that aims to segment the target object referred by a language expression in all
video frames. In this work, we propose a simple and unified framework built
upon Transformer, termed ReferFormer. It views the language as queries and
directly attends to the most relevant regions in the video frames. Concretely,
we introduce a small set of object queries conditioned on the language as the
input to the Transformer. In this manner, all the queries are obligated to find
the referred objects only. They are eventually transformed into dynamic kernels
which capture the crucial object-level information, and play the role of
convolution filters to generate the segmentation masks from feature maps. The
object tracking is achieved naturally by linking the corresponding queries
across frames. This mechanism greatly simplifies the pipeline and the
end-to-end framework is significantly different from the previous methods.
Extensive experiments on Ref-Youtube-VOS, Ref-DAVIS17, A2D-Sentences and
JHMDB-Sentences show the effectiveness of ReferFormer. On Ref-Youtube-VOS,
Refer-Former achieves 55.6J&F with a ResNet-50 backbone without bells and
whistles, which exceeds the previous state-of-the-art performance by 8.4
points. In addition, with the strong Swin-Large backbone, ReferFormer achieves
the best J&F of 64.2 among all existing methods. Moreover, we show the
impressive results of 55.0 mAP and 43.7 mAP on A2D-Sentences andJHMDB-Sentences
respectively, which significantly outperforms the previous methods by a large
margin. Code is publicly available at https://github.com/wjn922/ReferFormer.
