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.

通过语义辅助对象聚类（SOC）的多模态对比监督和强调时间上的连贯性，提出了一种在视频级别视觉语言对齐的增强下，对引用视频对象分割（RVOS）的研究，有效利用间帧关系和理解对象时间变化的文本描述。进行了大量的实验，并在所有基准测试中，我们的方法都以显著的优势优于现有的最先进竞争对手。