AI 中文总结
针对现有RGBT跟踪器融合参数无法适配目标状态变化的问题，提出PAFCNet，通过TA-HyperNet生成目标条件参数，实现动态融合与时序校准，在多RGBT跟踪基准上取得竞争力性能。
AI 中文摘要
现有RGBT跟踪器通常采用在不同目标和场景间参数固定的融合函数。尽管动态架构方法通过在预定义操作中选择提升了融合灵活性，但仍无法使融合参数适应不断变化的目标状态。为解决这些问题，我们提出用于RGBT跟踪的参数动态自适应融合与校准网络（PAFCNet）。PAFCNet动态生成目标条件参数，用于多模态融合与时序校准，使跟踪过程能适应目标外观变化和模态质量波动。具体而言，我们引入目标自适应超网络（TA-HyperNet），该网络利用模板表示（其在较少背景干扰下保留了稳定的目标身份和近期外观变化），为后续融合与校准生成目标条件参数。基于TA-HyperNet，我们设计了目标感知参数动态融合模块，该模块使用生成的参数调制融合过程，使融合模块能适应目标外观变化和复杂场景条件。此外，由于时空信息传播可能会累积跟踪噪声，我们提出动态时空校准模块，该模块利用TA-HyperNet为时空令牌生成校准参数。该模块在传播前动态校准历史信息，提升了时序表示的可靠性。实验结果表明，PAFCNet在多个RGBT跟踪基准上取得了具有竞争力的性能。
英文摘要
Existing RGBT trackers typically employ fusion functions with fixed parameters across different targets and scenarios. Although dynamic-architecture methods improve fusion flexibility by selecting among predefined operations, they still cannot adapt the fusion parameters to the evolving target state. To address these issues, we propose a Parameter-Dynamic Adaptive Fusion and Calibration Network (PAFCNet) for RGBT tracking. PAFCNet dynamically generates target-conditioned parameters for multimodal fusion and temporal calibration, enabling the tracking process to adapt to target appearance variations and modality quality fluctuations. Specifically, we introduce a Target-Adaptive Hypernetwork (TA-HyperNet) that leverages template representations, which preserve stable target identity and recent appearance changes with less background interference, to generate target-conditioned parameters for subsequent fusion and calibration. Based on TA-HyperNet, we design a target-aware parameter-dynamic fusion module that uses the generated parameters to modulate the fusion process. This enables the fusion module to adapt to changes in target appearance and complex scene conditions. Furthermore, since spatio-temporal information propagation may accumulate tracking noise, we propose a dynamic spatio-temporal calibration module that employs TA-HyperNet to generate calibration parameters for spatio-temporal tokens. By dynamically calibrating historical information before propagation, the module improves the reliability of temporal representations. Experimental results demonstrate that PAFCNet achieves competitive performance on multiple RGBT tracking benchmarks.
Comments9 pages,4 figures; Under review
