论文标题
进行快速原型制作和积极学习的可比性，以进行深度对象检测
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection
论文作者
论文摘要
在深度学习中，积极学习作为范式在涉及复杂的感知任务的应用中尤其重要，例如对象检测很难获得标签，而获取的标签很昂贵。在此类领域的积极学习方法的开发在高度计算上是昂贵且耗时的，它阻碍了研究的发展，并且导致方法之间缺乏可比性。在这项工作中，我们提出并调查了用于快速开发和对深度对象检测中主动学习的透明评估的沙盒设置。我们使用文献中发现的数据集和检测架构的常用配置的实验表明，在我们的沙盒环境中获得的结果代表了标准配置的结果。在与Pascal VOC进行比较时，与BDD100K相比，可以减少14个因素的总计算时间并评估学习行为，从而减少14个因素。这允许在不到半天的时间内测试和评估数据采集和标记策略，并有助于积极学习领域的透明度和开发速度以进行对象检测。
Active learning as a paradigm in deep learning is especially important in applications involving intricate perception tasks such as object detection where labels are difficult and expensive to acquire. Development of active learning methods in such fields is highly computationally expensive and time consuming which obstructs the progression of research and leads to a lack of comparability between methods. In this work, we propose and investigate a sandbox setup for rapid development and transparent evaluation of active learning in deep object detection. Our experiments with commonly used configurations of datasets and detection architectures found in the literature show that results obtained in our sandbox environment are representative of results on standard configurations. The total compute time to obtain results and assess the learning behavior can thereby be reduced by factors of up to 14 when comparing with Pascal VOC and up to 32 when comparing with BDD100k. This allows for testing and evaluating data acquisition and labeling strategies in under half a day and contributes to the transparency and development speed in the field of active learning for object detection.
