Video Object Segmentation with Dynamic Query Modulation
<|reference_start|>Video Object Segmentation with Dynamic Query Modulation: 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.<|reference_end|>
@article{zhou2024video,
    title={Video Object Segmentation with Dynamic Query Modulation},
    author={Hantao Zhou, Runze Hu, Xiu Li},
    journal={arXiv preprint arXiv:2403.11529},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11529},
    primaryClass={cs.CV}
}