  Referring video object segmentation (RVOS), as a supervised learning task,
relies on sufficient annotated data for a given scene. However, in more
realistic scenarios, only minimal annotations are available for a new scene,
which poses significant challenges to existing RVOS methods. With this in mind,
we propose a simple yet effective model with a newly designed cross-modal
affinity (CMA) module based on a Transformer architecture. The CMA module
builds multimodal affinity with a few samples, thus quickly learning new
semantic information, and enabling the model to adapt to different scenarios.
Since the proposed method targets limited samples for new scenes, we generalize
the problem as - few-shot referring video object segmentation (FS-RVOS). To
foster research in this direction, we build up a new FS-RVOS benchmark based on
currently available datasets. The benchmark covers a wide range and includes
multiple situations, which can maximally simulate real-world scenarios.
Extensive experiments show that our model adapts well to different scenarios
with only a few samples, reaching state-of-the-art performance on the
benchmark. On Mini-Ref-YouTube-VOS, our model achieves an average performance
of 53.1 J and 54.8 F, which are 10% better than the baselines. Furthermore, we
show impressive results of 77.7 J and 74.8 F on Mini-Ref-SAIL-VOS, which are
significantly better than the baselines. Code is publicly available at
https://github.com/hengliusky/Few_shot_RVOS.
