Automatic Adaptation of Object Detectors to New Domains Using Self Training
This work addresses the unsupervised adaptation of an existing object detector to a new target domain. We assume that a large number of unlabeled videos from this domain are readily available. We automatically obtain labels on the target data by using high confidence detections from the existing detector, augmented with hard (misclassified) examples acquired by exploiting temporal cues using a tracker. These automatically obtained labels are then used for re training the original model. A modified knowledge distillation loss is proposed, and we investigate several ways of assigning soft labels to the training examples from the target domain. Our approach is empirically evaluated on challenging face and pedestrian detection tasks: a face detector trained on WIDER Face, which consists of high quality images crawled from the web, is adapted to a large scale surveillance data set; a pedestrian detector trained on clear, daytime images from the BDD 100K driving data set is adapted to all other scenarios such as rainy, foggy, night time. Our results demonstrate the usefulness of incorporating hard examples obtained from tracking, the advantage of using soft labels via distillation loss versus hard labels, and show promising performance as a simple method for unsupervised domain adaptation of object detectors, with minimal dependence on hyper parameters.
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)