视频标注器：使用视觉语言模型和主动学习高效构建视频分类器的框架

Amir Ziai, Aneesh Vartakavi, Kelli Griggs, Eugene Lok, Yvonne Jukes, Alex Alonso, Vi Iyengar, Anna Pulido

Amir Ziai，Aneesh Vartakavi，Kelli Griggs，Eugene Lok，Yvonne Jukes，Alex Alonso，Vi Iyengar，Anna Pulido

Introduction

介绍

High-quality and consistent annotations are fundamental to the successful development of robust machine learning models. Conventional techniques for training machine learning classifiers are resource intensive. They involve a cycle where domain experts annotate a dataset, which is then transferred to data scientists to train models, review outcomes, and make changes. This labeling process tends to be time-consuming and inefficient, sometimes halting after a few annotation cycles.

高质量和一致的注释对于成功开发强大的机器学习模型至关重要。传统的机器学习分类器训练技术是资源密集型的。它们涉及一个循环，领域专家对数据集进行注释，然后将其转交给数据科学家进行模型训练、审查结果并进行更改。这个标注过程往往耗时且效率低下，有时在几个注释循环后就会停止。

Implications

影响

Consequently, less effort is invested in annotating high-quality datasets compared to iterating on complex models and algorithmic methods to improve performance and fix edge cases. As a result, ML systems grow rapidly in complexity.

因此，相比于迭代复杂模型和算法方法以提高性能和修复边缘情况，人们在注释高质量数据集方面投入的工作量较少。结果，机器学习系统的复杂性迅速增长。

Furthermore, constraints on time and resources often result in leveraging third-party annotators rather than domain experts. These annotators perform the labeling task without a deep understanding of the model’s intended deployment or usage, often making consistent labeling of borderline or hard examples, especially in more subjective tasks, a challenge.

此外，时间和资源的限制通常导致利用第三方注释者而不是领域专家。这些注释者在没有对模型的预期部署或使用有深入理解的情况下执行标注任务，这在更主观的任务中，尤其是在标注边界或困难示例时，会带来一些挑战。

This necessitates multiple review rounds with domain experts, leading to unexpected costs and delays. This lengthy cycle can also result in model drift, as it takes longer to fix edge cases and deploy new models, potentially hurting usefulness and stakeholder trust.

这需要与领域专家进行多次审查，导致意外的成本和延迟。这个漫长的循环也可能导致模型的漂移，因为修复边缘情况和部署新模型需要更长的时间，可能会损害其有用性和利益相关者的信任。

Solution

解决方案

We suggest that more direct involvement of domain experts, using a hu...