用跨域检测损失提升小目标头追踪精度，兼顾效率与性能。
Using Cross-Domain Detection Loss to Infer Multi-Scale Information for Improved Tiny Head Tracking
- 引入跨域检测损失，弥合大小检测器差距
- 多尺度模块捕捉高频细节，提升小目标检测
- 小感受野滤波器专精微小头部定位，适合资源受限场景
头部检测与追踪对下游任务至关重要，但现有方法通常需大量计算资源，导致延迟增加并占用处理器、内存和带宽。为此，我们提出一个框架，通过优化性能与效率的平衡，提升微型头部检测与追踪效果。该框架融合三项创新：(1) 跨域检测损失，(2) 多尺度模块，(3) 小感受野检测机制。这些设计有效缩小大、小检测器之间的差距，在训练中捕获多尺度高频细节，并利用小感受野滤波器实现对极小头部的精准定位。在CroHD和CrowdHuman数据集上的评估显示，该方法显著提升了多目标追踪准确率（MOTA）和平均精度（mAP），证明其在密集场景下的有效性。
原文摘要 · Abstract (English)
Head detection and tracking are essential for downstream tasks, but current methods often require large computational budgets, which increase latencies and ties up resources (e.g., processors, memory, and bandwidth). To address this, we propose a framework to enhance tiny head detection and tracking by optimizing the balance between performance and efficiency. Our framework integrates (1) a cross-domain detection loss, (2) a multi-scale module, and (3) a small receptive field detection mechanism. These innovations enhance detection by bridging the gap between large and small detectors, capturing high-frequency details at multiple scales during training, and using filters with small receptive fields to detect tiny heads. Evaluations on the CroHD and CrowdHuman datasets show improved Multiple Object Tracking Accuracy (MOTA) and mean Average Precision (mAP), demonstrating the effectiveness of our approach in crowded scenes.
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