VIRST：用于时空分割的视频指导推理助手
VIRST: Video-Instructed Reasoning Assistant for SpatioTemporal Segmentation
AI总结：
VIRST提出一种端到端框架，统一全局视频推理与像素级分割预测，通过时空融合模块和时间动态锚点更新器，提升动态场景下的分割性能。
摘要：
参考视频对象分割（RVOS）旨在基于自然语言描述分割视频中的目标对象。然而，固定关键帧方法在处理快速变化的时空动态和需要多步推理的查询时表现不佳，导致性能下降。为解决这些问题，我们提出了VIRST（用于时空分割的视频指导推理助手），该框架通过时空融合模块将语义和分割表示连接起来，并采用时间动态锚点更新器维持时间相邻的锚点帧，以在大运动、遮挡和重现情况下提供稳定的时序线索。这种统一设计在多种RVOS基准测试中实现了最先进的结果，证明了其在参考和推理导向场景中的强大泛化能力。代码和检查点可在https://github.com/AIDASLab/VIRST获取。
英文摘要：
Referring Video Object Segmentation (RVOS) aims to segment target objects in videos based on natural language descriptions. However, fixed keyframe-based approaches that couple a vision language model with a separate propagation module often fail to capture rapidly changing spatiotemporal dynamics and to handle queries requiring multi-step reasoning, leading to sharp performance drops on motion-intensive and reasoning-oriented videos beyond static RVOS benchmarks. To address these limitations, we propose VIRST (Video-Instructed Reasoning Assistant for Spatio-Temporal Segmentation), an end-to-end framework that unifies global video reasoning and pixel-level mask prediction within a single model. VIRST bridges semantic and segmentation representations through the Spatio-Temporal Fusion (STF), which fuses segmentation-aware video features into the vision-language backbone, and employs the Temporal Dynamic Anchor Updater to maintain temporally adjacent anchor frames that provide stable temporal cues under large motion, occlusion, and reappearance. This unified design achieves state-of-the-art results across diverse RVOS benchmarks under realistic and challenging conditions, demonstrating strong generalization to both referring and reasoning oriented settings. The code and checkpoints are available at https://github.com/AIDASLab/VIRST.
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