  Referring video object segmentation aims to segment the object referred by a
given language expression. Existing works typically require compressed video
bitstream to be decoded to RGB frames before being segmented, which increases
computation and storage requirements and ultimately slows the inference down.
This may hamper its application in real-world computing resource limited
scenarios, such as autonomous cars and drones. To alleviate this problem, in
this paper, we explore the referring object segmentation task on compressed
videos, namely on the original video data flow. Besides the inherent difficulty
of the video referring object segmentation task itself, obtaining
discriminative representation from compressed video is also rather challenging.
To address this problem, we propose a multi-attention network which consists of
dual-path dual-attention module and a query-based cross-modal Transformer
module. Specifically, the dual-path dual-attention module is designed to
extract effective representation from compressed data in three modalities,
i.e., I-frame, Motion Vector and Residual. The query-based cross-modal
Transformer firstly models the correlation between linguistic and visual
modalities, and then the fused multi-modality features are used to guide object
queries to generate a content-aware dynamic kernel and to predict final
segmentation masks. Different from previous works, we propose to learn just one
kernel, which thus removes the complicated post mask-matching procedure of
existing methods. Extensive promising experimental results on three challenging
datasets show the effectiveness of our method compared against several
state-of-the-art methods which are proposed for processing RGB data. Source
code is available at: https://github.com/DexiangHong/MANet.
