Recently, state space models have demonstrated efficient video segmentation through linear-complexity state space compression. However, Video Semantic Segmentation (VSS) requires pixel-level spatiotemporal modeling capabilities to maintain temporal consistency in segmentation of semantic objects. While state space models can preserve common semantic information during state space compression, the fixed-size state space inevitably forgets specific information, which limits the models' capability for pixel-level segmentation. To tackle the above issue, we proposed a Refining Specifics State Space Model approach (RS-SSM) for video semantic segmentation, which performs complementary refining of forgotten spatiotemporal specifics. Specifically, a Channel-wise Amplitude Perceptron (CwAP) is designed to extract and align the distribution characteristics of specific information in the state space. Besides, a Forgetting Gate Information Refiner (FGIR) is proposed to adaptively invert and refine the forgetting gate matrix in the state space model based on the specific information distribution. Consequently, our RS-SSM leverages the inverted forgetting gate to complementarily refine the specific information forgotten during state space compression, thereby enhancing the model's capability for spatiotemporal pixel-level segmentation. Extensive experiments on four VSS benchmarks demonstrate that our RS-SSM achieves state-of-the-art performance while maintaining high computational efficiency. The code is available at https://github.com/zhoujiahuan1991/CVPR2026-RS-SSM.

翻译：近期，状态空间模型通过线性复杂度的状态空间压缩展示了高效的视频分割能力。然而，视频语义分割（VSS）需要像素级的时空建模能力，以维持语义对象分割的时间一致性。尽管状态空间模型在状态空间压缩过程中能够保留公共语义信息，但固定大小的状态空间不可避免地会遗忘特定信息，这限制了模型在像素级分割中的能力。为解决上述问题，我们提出了一种用于视频语义分割的精炼特定细节状态空间模型方法（RS-SSM），该方法对遗忘的时空特定细节进行互补性精炼。具体而言，我们设计了一个通道级幅度感知器（CwAP），用于提取和对齐状态空间中特定信息的分布特征。此外，我们还提出了一种遗忘门信息精炼器（FGIR），基于特定信息分布自适应地反转和精炼状态空间模型中的遗忘门矩阵。由此，我们的RS-SSM利用反转的遗忘门对状态空间压缩过程中遗忘的特定细节进行互补性精炼，从而增强了模型在时空像素级分割中的能力。在四个VSS基准上的大量实验表明，我们的RS-SSM在保持高计算效率的同时，实现了最先进的性能。代码已在https://github.com/zhoujiahuan1991/CVPR2026-RS-SSM公开。