发表机构
Cisco Systems, Inc.（思科系统公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于VLM的智能体调谐器，无需目标域标签即可实现跨域跟踪器适配，在MOT17→MOT20任务中可恢复67.8%的损失性能，且操作具有选择性。
AI 中文摘要
我们提出一种系统，该系统使用视觉语言模型（Vision-Language Model，VLM）作为诊断智能体，在无法获取目标域标签的情况下，将检测到跟踪的流水线适配到新的目标域。该系统并非针对标注指标进行优化，而是由VLM直接检查渲染后的跟踪输出，识别视觉失败模式，并通过迭代调优循环推荐参数更新。我们首先证明，由真值监督的超参数迁移可能存在脆弱性：在MOT17→MOT20任务中，应用源域导出的最优配置会使平均HOTA指标从目标域上限的0.357降至0.267，降幅达0.090。在不使用任何目标域标签的情况下，我们的基于VLM的调谐器恢复了这部分损失性能的67.8%，最终HOTA指标与目标域上限仅相差0.029；在密度最高的目标序列上，其恢复比例可达86.7%。我们进一步表明，带有手工代理目标的无标签贝叶斯优化在大域偏移下表现不佳，甚至可能降低已有的优良配置。相比之下，VLM调谐器的操作具有选择性：当视觉诊断未发现明确失败模式时，它会拒绝修改配置，在保留易迁移任务性能的同时改进难迁移任务。最后，我们明确了该方法成功的条件：当域偏移通过暴露的检测级参数体现时，方法有效；而在源域最优配置已接近最优的场景（如MOT17→DanceTrack），方法效果较差。
英文摘要
We present a system that uses a Vision-Language Model (VLM) as a diagnostic agent for adapting a detect-to-track pipeline to a new target domain without access to target-domain labels. Rather than optimizing against annotated metrics, the VLM directly inspects rendered tracking outputs, identifies visual failure modes, and recommends parameter updates through an iterative tuning loop. We first demonstrate that ground-truth-supervised hyperparameter transfer can be brittle. On MOT17->MOT20, applying a source-derived oracle configuration reduces mean HOTA by 0.090, from a target-domain ceiling of 0.357, to 0.267. Without using any target-domain labels, our VLM-based tuner recovers 67.8% of this lost headroom, finishing within 0.029 HOTA of the target ceiling; on the highest-density target sequence, it recovers up to 86.7%. We further show that label-free Bayesian optimization with handcrafted proxy objectives struggles under large domain shifts and can degrade configurations that are already strong. In contrast, the VLM tuner acts selectively: when its visual diagnosis reveals no clear failure mode, it declines to modify the configuration, preserving performance on easy transfers while improving hard ones. Finally, we characterize the conditions under which this approach succeeds, namely, when domain shift manifests through exposed detection-level parameters, versus where it is less effective, such as MOT17->DanceTrack, where the source oracle is already near-optimal.
