SMTrack: 基于状态的Mamba用于视觉跟踪中的高效时序建模
SMTrack: State-Aware Mamba for Efficient Temporal Modeling in Visual Tracking
AI总结：
SMTrack提出了一种基于状态的Mamba模型，通过线性计算复杂度实现高效视觉跟踪，无需复杂模块或高计算成本，提升跟踪鲁棒性。
摘要：
视觉跟踪旨在自动估计视频序列中目标物体的状态，这在动态场景中尤为具有挑战性。因此，提出了许多方法来引入时间线索以提高跟踪的鲁棒性。然而，传统CNN和Transformer架构在建模视觉跟踪中的长距离时间依赖性时存在固有的局限性，通常需要复杂的定制模块或显著的计算成本来整合时间线索。受状态空间模型的成功启发，我们提出了一种新的视觉跟踪时序建模范式，称为状态感知Mamba跟踪器（SMTrack），提供了一个简洁的训练和跟踪流程，无需需要定制模块或显著的计算成本来构建长距离时间依赖性。它具有几个优点。首先，我们提出了一种新的选择性状态感知空间模型，具有状态级参数，以捕捉更多多样的时间线索以实现鲁棒的跟踪。第二，SMTrack在训练期间通过线性计算复杂度实现长距离时间交互。第三，SMTrack通过隐藏状态传播和更新，使每个帧能够与之前跟踪的帧进行交互，这在跟踪过程中释放了处理时间线索的计算成本。广泛的实验结果表明，SMTrack在低计算成本下实现了有希望的性能。
英文摘要：
Visual tracking aims to automatically estimate the state of a target object in a video sequence, which is challenging especially in dynamic scenarios. Thus, numerous methods are proposed to introduce temporal cues to enhance tracking robustness. However, conventional CNN and Transformer architectures exhibit inherent limitations in modeling long-range temporal dependencies in visual tracking, often necessitating either complex customized modules or substantial computational costs to integrate temporal cues. Inspired by the success of the state space model, we propose a novel temporal modeling paradigm for visual tracking, termed State-aware Mamba Tracker (SMTrack), providing a neat pipeline for training and tracking without needing customized modules or substantial computational costs to build long-range temporal dependencies. It enjoys several merits. First, we propose a novel selective state-aware space model with state-wise parameters to capture more diverse temporal cues for robust tracking. Second, SMTrack facilitates long-range temporal interactions with linear computational complexity during training. Third, SMTrack enables each frame to interact with previously tracked frames via hidden state propagation and updating, which releases computational costs of handling temporal cues during tracking. Extensive experimental results demonstrate that SMTrack achieves promising performance with low computational costs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
