Domain Adaptive Object Detection

Domain adaptive object detection focuses on training object detectors that generalize well to new, unseen domains without requiring extensive labeled data in those domains. Current research emphasizes techniques like adversarial learning, self-training with pseudo-labels (often refined using uncertainty estimation or adversarial attacks), and feature alignment at multiple granularities (pixel, instance, category levels), often within transformer or other deep learning architectures. These advancements are crucial for deploying object detectors in real-world scenarios with significant variations in visual appearance (e.g., different weather conditions, sensor modalities), improving the robustness and reliability of computer vision systems in diverse applications like autonomous driving and medical image analysis.