  Referring video object segmentation aims to predict foreground labels for
objects referred by natural language expressions in videos. Previous methods
either depend on 3D ConvNets or incorporate additional 2D ConvNets as encoders
to extract mixed spatial-temporal features. However, these methods suffer from
spatial misalignment or false distractors due to delayed and implicit
spatial-temporal interaction occurring in the decoding phase. To tackle these
limitations, we propose a Language-Bridged Duplex Transfer (LBDT) module which
utilizes language as an intermediary bridge to accomplish explicit and adaptive
spatial-temporal interaction earlier in the encoding phase. Concretely,
cross-modal attention is performed among the temporal encoder, referring words
and the spatial encoder to aggregate and transfer language-relevant motion and
appearance information. In addition, we also propose a Bilateral Channel
Activation (BCA) module in the decoding phase for further denoising and
highlighting the spatial-temporal consistent features via channel-wise
activation. Extensive experiments show our method achieves new state-of-the-art
performances on four popular benchmarks with 6.8% and 6.9% absolute AP gains on
A2D Sentences and J-HMDB Sentences respectively, while consuming around 7x less
computational overhead.
