论文标题
自适应自我训练以检测对象
Adaptive Self-Training for Object Detection
论文作者
论文摘要
深度学习已成为解决图像中对象检测任务的有效解决方案，但需要大量标记的数据集。为了减轻这种成本，已经提出了一个半监督对象检测方法，这些方法在利用丰富的未标记数据组成，并且已经提出了令人印象深刻的结果。但是，这些方法中的大多数都需要通过阈值将伪标签链接到地面对象。在以前的工作中，通常会从经验上确定此阈值值，这很耗时，仅用于单个数据分布。当域（并因此需要数据分布）发生变化时，需要进行新的且昂贵的参数搜索。在这项工作中，我们介绍了我们的方法自适应自我训练以进行对象检测（ASTOD），这是一种简单而有效的教师研究方法。 ASTOD可以直接基于得分直方图的地面值而无需成本一个阈值。为了提高教师预测的质量，我们还提出了一种新颖的伪标记程序。在伪标记的步骤中，我们使用未标记的图像的不同视图来减少错过的预测数量，从而获得更好的候选标签。我们的老师和我们的学生接受了单独培训，我们的方法可以通过学生代替老师来以迭代方式使用。在MS-COCO数据集上，我们的方法始终如一地针对不需要阈值参数的最新方法，并使用需要参数扫描搜索的方法显示竞争结果。关于包含卫星图像的DIOR数据集的监督基线的其他实验导致了类似的结论，并证明无论数据分布如何，都可以自动适应自动训练的分数阈值。该代码可在https：// github.com/rvandeghen/astod上找到
Deep learning has emerged as an effective solution for solving the task of object detection in images but at the cost of requiring large labeled datasets. To mitigate this cost, semi-supervised object detection methods, which consist in leveraging abundant unlabeled data, have been proposed and have already shown impressive results. However, most of these methods require linking a pseudo-label to a ground-truth object by thresholding. In previous works, this threshold value is usually determined empirically, which is time consuming, and only done for a single data distribution. When the domain, and thus the data distribution, changes, a new and costly parameter search is necessary. In this work, we introduce our method Adaptive Self-Training for Object Detection (ASTOD), which is a simple yet effective teacher-student method. ASTOD determines without cost a threshold value based directly on the ground value of the score histogram. To improve the quality of the teacher predictions, we also propose a novel pseudo-labeling procedure. We use different views of the unlabeled images during the pseudo-labeling step to reduce the number of missed predictions and thus obtain better candidate labels. Our teacher and our student are trained separately, and our method can be used in an iterative fashion by replacing the teacher by the student. On the MS-COCO dataset, our method consistently performs favorably against state-of-the-art methods that do not require a threshold parameter, and shows competitive results with methods that require a parameter sweep search. Additional experiments with respect to a supervised baseline on the DIOR dataset containing satellite images lead to similar conclusions, and prove that it is possible to adapt the score threshold automatically in self-training, regardless of the data distribution. The code is available at https:// github.com/rvandeghen/ASTOD
