解决视频目标分割中查询选择偏差问题，提升定位准确性。
Mitigating Query Selection Bias in Referring Video Object Segmentation
- 将查询分解为外观、空间关系和运动三个动态组件，融合语言与视觉信息。
- 在多个基准上实现更优性能，显著减少因外观或运动相似导致的误匹配。
- 适合关注跨模态对齐与视频理解的科研人员和开发者。
近期基于查询的方法在指代视频目标分割（RVOS）中表现优异，通过文本静态查询驱动跨模态对齐。然而，这些静态查询易受外观或运动相似的干扰项误导，产生查询选择偏差。为此，我们提出三重查询生成器（TQF），将指代查询分解为三个专用组件：用于静态属性的外观查询、用于帧内空间关系的交互查询、以及用于帧间时序关联的运动查询。查询不依赖单一文本嵌入，而是结合语言线索与视觉引导动态构建。此外，我们设计两个运动感知聚合模块：帧内交互聚合利用位置感知的物体间交互，帧间运动聚合则通过轨迹引导对齐，确保时序一致性。在多个RVOS基准上的大量实验表明，TQF在结构化查询设计与运动感知聚合模块方面均具优势。
原文摘要 · Abstract (English)
Recently, query-based methods have achieved remarkable performance in Referring Video Object Segmentation (RVOS) by using textual static object queries to drive cross-modal alignment. However, these static queries are easily misled by distractors with similar appearance or motion, resulting in \emph{query selection bias}. To address this issue, we propose Triple Query Former (TQF), which factorizes the referring query into three specialized components: an appearance query for static attributes, an intra-frame interaction query for spatial relations, and an inter-frame motion query for temporal association. Instead of relying solely on textual embeddings, our queries are dynamically constructed by integrating both linguistic cues and visual guidance. Furthermore, we introduce two motion-aware aggregation modules that enhance object token representations: Intra-frame Interaction Aggregation incorporates position-aware interactions among objects within a single frame, while Inter-frame Motion Aggregation leverages trajectory-guided alignment across frames to ensure temporal coherence. Extensive experiments on multiple RVOS benchmarks demonstrate the advantages of TQF and the effectiveness of our structured query design and motion-aware aggregation modules.
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