  This paper studies referring video object segmentation (RVOS) by boosting
video-level visual-linguistic alignment. Recent approaches model the RVOS task
as a sequence prediction problem and perform multi-modal interaction as well as
segmentation for each frame separately. However, the lack of a global view of
video content leads to difficulties in effectively utilizing inter-frame
relationships and understanding textual descriptions of object temporal
variations. To address this issue, we propose Semantic-assisted Object Cluster
(SOC), which aggregates video content and textual guidance for unified temporal
modeling and cross-modal alignment. By associating a group of frame-level
object embeddings with language tokens, SOC facilitates joint space learning
across modalities and time steps. Moreover, we present multi-modal contrastive
supervision to help construct well-aligned joint space at the video level. We
conduct extensive experiments on popular RVOS benchmarks, and our method
outperforms state-of-the-art competitors on all benchmarks by a remarkable
margin. Besides, the emphasis on temporal coherence enhances the segmentation
stability and adaptability of our method in processing text expressions with
temporal variations. Code will be available.

Computer Vision and Pattern Recognition