Temporal Collection and Distribution for Referring Video Object
  Segmentation
Jiajin Tang Ge Zheng Sibei Yang
Referring video object segmentation aims to segment a referent throughout a
video sequence according to a natural language expression. It requires aligning
the natural language expression with the objects' motions and their dynamic
associations at the global video level but segmenting objects at the frame
level. To achieve this goal, we propose to simultaneously maintain a global
referent token and a sequence of object queries, where the former is
responsible for capturing video-level referent according to the language
expression, while the latter serves to better locate and segment objects with
each frame. Furthermore, to explicitly capture object motions and
spatial-temporal cross-modal reasoning over objects, we propose a novel
temporal collection-distribution mechanism for interacting between the global
referent token and object queries. Specifically, the temporal collection
mechanism collects global information for the referent token from object
queries to the temporal motions to the language expression. In turn, the
temporal distribution first distributes the referent token to the referent
sequence across all frames and then performs efficient cross-frame reasoning
between the referent sequence and object queries in every frame. Experimental
results show that our method outperforms state-of-the-art methods on all
benchmarks consistently and significantly.