Downstream Object Detection

Downstream object detection focuses on improving the accuracy and efficiency of object detection models by leveraging pre-training techniques and incorporating diverse data sources. Current research emphasizes strategies like contrastive learning, which enhances robustness to domain shifts by carefully designing training views, and self-supervised pre-training of both feature extractors and region proposal networks to reduce the need for large labeled datasets. These advancements, often incorporating transformer architectures and language models, aim to improve performance in challenging scenarios such as dense scenes and limited annotation availability, with significant implications for applications like autonomous driving and robotics.