  Storing intermediate frame segmentations as memory for long-range context
modeling, spatial-temporal memory-based methods have recently showcased
impressive results in semi-supervised video object segmentation (SVOS).
However, these methods face two key limitations: 1) relying on non-local
pixel-level matching to read memory, resulting in noisy retrieved features for
segmentation; 2) segmenting each object independently without interaction.
These shortcomings make the memory-based methods struggle in similar object and
multi-object segmentation. To address these issues, we propose a query
modulation method, termed QMVOS. This method summarizes object features into
dynamic queries and then treats them as dynamic filters for mask prediction,
thereby providing high-level descriptions and object-level perception for the
model. Efficient and effective multi-object interactions are realized through
inter-query attention. Extensive experiments demonstrate that our method can
bring significant improvements to the memory-based SVOS method and achieve
competitive performance on standard SVOS benchmarks. The code is available at
https://github.com/zht8506/QMVOS.
