论文标题
跨数据库培训用于增加对象检测
Cross-dataset Training for Class Increasing Object Detection
论文作者
论文摘要
我们提出了一个概念上简单，灵活和一般的框架，用于对象检测中的跨数据库训练。给定两个或多个已经标记为不同对象类的数据集，跨数据库培训旨在检测不同类的结合，因此我们不必为所有数据集标记所有类。通过跨数据库培训，可以使用现有数据集用单个模型来检测合并的对象类。此外，在工业应用中，对象类通常会按需增加。因此，在添加新类时，如果我们在所有现有数据集上标记新类，那将是非常耗时的。在使用跨数据库培训时，我们只需要在新数据集上标记新类。我们在Pascal VOC，可可，更宽的脸和更宽的行人上进行实验，并具有独奏和跨数据库设置。结果表明，与独立培训相比，我们的跨数据库管道可以在这些数据集上同时实现相似的令人印象深刻的性能。
We present a conceptually simple, flexible and general framework for cross-dataset training in object detection. Given two or more already labeled datasets that target for different object classes, cross-dataset training aims to detect the union of the different classes, so that we do not have to label all the classes for all the datasets. By cross-dataset training, existing datasets can be utilized to detect the merged object classes with a single model. Further more, in industrial applications, the object classes usually increase on demand. So when adding new classes, it is quite time-consuming if we label the new classes on all the existing datasets. While using cross-dataset training, we only need to label the new classes on the new dataset. We experiment on PASCAL VOC, COCO, WIDER FACE and WIDER Pedestrian with both solo and cross-dataset settings. Results show that our cross-dataset pipeline can achieve similar impressive performance simultaneously on these datasets compared with training independently.
