Domain Adaptive Object Detection via Balancing Between Self-Training and
  Adversarial Learning
Muhammad Akhtar Munir Muhammad Haris Khan M. Saquib Sarfraz Mohsen Ali
Deep learning based object detectors struggle generalizing to a new target
domain bearing significant variations in object and background. Most current
methods align domains by using image or instance-level adversarial feature
alignment. This often suffers due to unwanted background and lacks
class-specific alignment. A straightforward approach to promote class-level
alignment is to use high confidence predictions on unlabeled domain as
pseudo-labels. These predictions are often noisy since model is poorly
calibrated under domain shift. In this paper, we propose to leverage model's
predictive uncertainty to strike the right balance between adversarial feature
alignment and class-level alignment. We develop a technique to quantify
predictive uncertainty on class assignments and bounding-box predictions. Model
predictions with low uncertainty are used to generate pseudo-labels for
self-training, whereas the ones with higher uncertainty are used to generate
tiles for adversarial feature alignment. This synergy between tiling around
uncertain object regions and generating pseudo-labels from highly certain
object regions allows capturing both image and instance-level context during
the model adaptation. We report thorough ablation study to reveal the impact of
different components in our approach. Results on five diverse and challenging
adaptation scenarios show that our approach outperforms existing
state-of-the-art methods with noticeable margins.