Adapting Vehicle Detector to Target Domain by Adversarial Prediction Alignment
This work addresses performance degradation in object detection for satellite imagery, but it is incremental as it builds on existing domain adaptation techniques.
The paper tackled the problem of domain adaptation for object detection by aligning prediction outputs in addition to features, specifically for vehicle detection in satellite images, resulting in a significant improvement of over 5% in AP score.
While recent advancement of domain adaptation techniques is significant, most of methods only align a feature extractor and do not adapt a classifier to target domain, which would be a cause of performance degradation. We propose novel domain adaptation technique for object detection that aligns prediction output space. In addition to feature alignment, we aligned predictions of locations and class confidences of our vehicle detector for satellite images by adversarial training. The proposed method significantly improved AP score by over 5%, which shows effectivity of our method for object detection tasks in satellite images.