Deep Domain Adaptive Object Detection: a Survey

Understand

Deep learning (DL) based object detection has achieved great progress.

• These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution.
• However, the two assumptions are not always hold in practice.
• Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges.