← 研踪 PaperTrailarXiv:2611.15059 · v1
Test-Time Selective Adaptation for Cross-Modal UAV Object Tracking with Token Shift
首次提交 2026-11-11 13:32
版本更新 2026-11-11 13:32
雷达观测 2026-11-13 11:11
摘要
Cross-modal unmanned aerial vehicle (UAV) object tracking faces modality-induced appearance discrepancies and abrupt target-position shifts that can arise from modality switching, UAV motion, and inter-sensor offsets. Although existing methods model modality and spatial variations, they primarily rely on offline representation learning and offer limited localization-oriented adaptation during testing. To address these challenges, we propose Test-Time Selective Adaptation with Token Shift (TSAT), which derives spatial and temporal adaptation signals from unlabeled test sequences for online optimization. Specifically, we design a controllable token shift auxiliary task, which imposes explicit spatial correspondence between predefined token translations and resultant localization offsets, generating stable annotation-free spatial adaptation signals. Furthermore, we introduce a temporal backward prediction auxiliary task to exploit frame-wise temporal dependencies, effectively mitigating cumulative localization errors in continuous tracking. To suppress interference from unreliable tracking samples, we develop a reliability-aware update gating strategy to screen valid adaptation signals and stabilize online parameter updates. Experiments on CM-UOT demonstrate that TSAT achieves state-of-the-art performance against existing test-time adaptation methods, exhibiting superior robustness under frequent modality switching and severe spatial displacement. Experiments across tracking frameworks and datasets further validate the effectiveness and generalization capability of TSAT.
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v1 · 2026-11-11 13:32 · 最新观测版本
Test-Time Selective Adaptation for Cross-Modal UAV Object Tracking with Token Shift
Cross-modal unmanned aerial vehicle (UAV) object tracking faces modality-induced appearance discrepancies and abrupt target-position shifts that can arise from modality switching, UAV motion, and inter-sensor offsets. Although existing methods model modality and spatial variations, they primarily rely on offline representation learning and offer limited localization-oriented adaptation during testing. To address these challenges, we propose Test-Time Selective Adaptation with Token Shift (TSAT), which derives spatial and temporal adaptation signals from unlabeled test sequences for online optimization. Specifically, we design a controllable token shift auxiliary task, which imposes explicit spatial correspondence between predefined token translations and resultant localization offsets, generating stable annotation-free spatial adaptation signals. Furthermore, we introduce a temporal backward prediction auxiliary task to exploit frame-wise temporal dependencies, effectively mitigating cumulative localization errors in continuous tracking. To suppress interference from unreliable tracking samples, we develop a reliability-aware update gating strategy to screen valid adaptation signals and stabilize online parameter updates. Experiments on CM-UOT demonstrate that TSAT achieves state-of-the-art performance against existing test-time adaptation methods, exhibiting superior robustness under frequent modality switching and severe spatial displacement. Experiments across tracking frameworks and datasets further validate the effectiveness and generalization capability of TSAT.
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