  Tracking and segmenting multiple similar objects with complex or separate
parts in long-term videos is inherently challenging due to the ambiguity of
target parts and identity confusion caused by occlusion, background clutter,
and long-term variations. In this paper, we propose a robust video object
segmentation framework equipped with spatial-semantic features and
discriminative object queries to address the above issues. Specifically, we
construct a spatial-semantic network comprising a semantic embedding block and
spatial dependencies modeling block to associate the pretrained ViT features
with global semantic features and local spatial features, providing a
comprehensive target representation. In addition, we develop a masked
cross-attention module to generate object queries that focus on the most
discriminative parts of target objects during query propagation, alleviating
noise accumulation and ensuring effective long-term query propagation. The
experimental results show that the proposed method set a new state-of-the-art
performance on multiple datasets, including the DAVIS2017 test (89.1%),
YoutubeVOS 2019 (88.5%), MOSE (75.1%), LVOS test (73.0%), and LVOS val (75.1%),
which demonstrate the effectiveness and generalization capacity of the proposed
method. We will make all source code and trained models publicly available.
