Adaptive Object Detection

Adaptive object detection focuses on improving the robustness of object detectors when deployed in environments significantly different from their training data. Current research emphasizes techniques like self-training with pseudo-labels, domain alignment through adversarial learning or feature-level adjustments, and the use of transformer-based architectures to bridge domain gaps. These advancements are crucial for deploying object detection in real-world scenarios, such as autonomous driving and medical image analysis, where data distributions are inherently variable and labeled data may be scarce or unavailable.