用大模型辅助标注动物追踪数据，效果不如人工+自动结合
No Free Lunch in Annotation either: An objective evaluation of foundation models for streamlining annotation in animal tracking
- 对比全自动与人工+自动混合标注策略
- 混合方式获80.8的IDF1，全自动仅65.6
- 强调质量控制对追踪模型关键性
我们评估了基础模型在简化动物追踪标注任务中的表现。大量高质量标注数据对追踪模型的鲁棒性至关重要，尤其在长时序行为分析中。然而，过度依赖自动化标注可能适得其反，低质标注会引入噪声，损害模型性能。实验表明，谨慎结合自动化与人工标注可显著提升效果：混合策略在IDF1上达到80.8，远超盲目使用SAM2视频模型的65.6。因此，精细化的质量控制是实现高效标注的关键。
原文摘要 · Abstract (English)
We analyze the capabilities of foundation models addressing the tedious task of generating annotations for animal tracking. Annotating a large amount of data is vital and can be a make-or-break factor for the robustness of a tracking model. Robustness is particularly crucial in animal tracking, as accurate tracking over long time horizons is essential for capturing the behavior of animals. However, generating additional annotations using foundation models can be counterproductive, as the quality of the annotations is just as important. Poorly annotated data can introduce noise and inaccuracies, ultimately compromising the performance and accuracy of the trained model. Over-reliance on automated annotations without ensuring precision can lead to diminished results, making careful oversight and quality control essential in the annotation process. Ultimately, we demonstrate that a thoughtful combination of automated annotations and manually annotated data is a valuable strategy, yielding an IDF1 score of 80.8 against blind usage of SAM2 video with an IDF1 score of 65.6.
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