Video Object Segmentation with Dynamic Query Modulation

• URL: http://arxiv.org/abs/2403.11529v1
• Date: Mon, 18 Mar 2024 07:31:39 GMT
• Title: Video Object Segmentation with Dynamic Query Modulation
• Authors: Hantao Zhou, Runze Hu, Xiu Li,
• Abstract summary: We propose a query modulation method, termed QMVOS, for object and multi-object segmentation. Our method can bring significant improvements to the memory-based SVOS method and achieve competitive performance on standard SVOS benchmarks.
• Score: 23.811776213359625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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