  Referring video object segmentation (RVOS) relies on natural language
expressions to segment target objects in video, emphasizing modeling dense
text-video relations. The current RVOS methods typically use independently
pre-trained vision and language models as backbones, resulting in a significant
domain gap between video and text. In cross-modal feature interaction, text
features are only used as query initialization and do not fully utilize
important information in the text. In this work, we propose using frozen
pre-trained vision-language models (VLM) as backbones, with a specific emphasis
on enhancing cross-modal feature interaction. Firstly, we use frozen
convolutional CLIP backbone to generate feature-aligned vision and text
features, alleviating the issue of domain gap and reducing training costs.
Secondly, we add more cross-modal feature fusion in the pipeline to enhance the
utilization of multi-modal information. Furthermore, we propose a novel video
query initialization method to generate higher quality video queries. Without
bells and whistles, our method achieved 51.5 J&F on the MeViS test set and
ranked 3rd place for MeViS Track in CVPR 2024 PVUW workshop: Motion Expression
guided Video Segmentation.
