In this paper, we propose subspace alignment based domain adaptation of the state of the art RCNN based object detector. The aim is to be able to achieve high quality object detection in novel, real world target scenarios without requiring labels from the target domain. While, unsupervised domain adaptation has been studied in the case of object classification, for object detection it has been relatively unexplored. In subspace based domain adaptation for objects, we need access to source and target subspaces for the bounding box features. The absence of supervision (labels and bounding boxes are absent) makes the task challenging. In this paper, we show that we can still adapt sub- spaces that are localized to the object by obtaining detections from the RCNN detector trained on source and applied on target. Then we form localized subspaces from the detections and show that subspace alignment based adaptation between these subspaces yields improved object detection. This evaluation is done by considering challenging real world datasets of PASCAL VOC as source and validation set of Microsoft COCO dataset as target for various categories.

本文介绍了基于子空间对齐的领域自适应方法，用以实现在没有来自目标领域的标签情况下，在新的实际目标场景中实现高质量的物体检测。我们将RCNN目标检测器和子空间适应方法相结合，通过获取来自源域和目标域的检测结果来实现对局部映射的适应，从而提高物体检测的性能。我们在PASCAL VOC和Microsoft COCO等现实世界中的数据集上进行了评价。

基于子空间对齐的RCNN检测器域自适应