Abstract
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 this https URL.
Abstract (translated)
最近，状态空间模型通过线性复杂度的状态空间压缩，展现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，以保持语义对象分割的时间一致性。虽然状态空间模型能在压缩过程中保留通用语义信息，但固定大小的状态空间不可避免地会遗忘具体信息，这限制了模型在像素级分割上的能力。针对上述问题，我们提出了一种用于视频语义分割的细化细节状态空间模型方法（RS-SSM），该方法对遗忘的时空细节进行互补细化。具体而言，设计了通道幅度感知机（CwAP）来提取和对齐状态空间中具体信息的分布特征。此外，提出了遗忘门信息细化器（FGIR），基于具体信息分布自适应反转并细化状态空间模型中的遗忘门矩阵。因此，我们的RS-SSM利用反转后的遗忘门，对状态空间压缩过程中被遗忘的具体信息进行互补细化，从而增强模型的时空像素级分割能力。在四个VSS基准测试上的大量实验表明，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。代码可在以下链接获取：https URL。
URL
https://arxiv.org/abs/2603.24295
