  Referring Video Object Segmentation (R-VOS) methods face challenges in
maintaining consistent object segmentation due to temporal context variability
and the presence of other visually similar objects. We propose an end-to-end
R-VOS paradigm that explicitly models temporal instance consistency alongside
the referring segmentation. Specifically, we introduce a novel hybrid memory
that facilitates inter-frame collaboration for robust spatio-temporal matching
and propagation. Features of frames with automatically generated high-quality
reference masks are propagated to segment the remaining frames based on
multi-granularity association to achieve temporally consistent R-VOS.
Furthermore, we propose a new Mask Consistency Score (MCS) metric to evaluate
the temporal consistency of video segmentation. Extensive experiments
demonstrate that our approach enhances temporal consistency by a significant
margin, leading to top-ranked performance on popular R-VOS benchmarks, i.e.,
Ref-YouTube-VOS (67.1%) and Ref-DAVIS17 (65.6%).
