Pre Trained Object

Pre-trained object detection models are foundational to many computer vision tasks, serving as efficient starting points for various applications. Current research focuses on improving their performance in challenging scenarios, such as handling distorted panoramic imagery, adapting to adverse conditions (e.g., low light, noise), and efficiently transferring them to new domains with limited labeled data. This involves exploring novel training methodologies using synthetic data, incorporating transformer architectures, and developing uncertainty-aware active learning strategies. The resulting advancements have significant implications for diverse fields, including autonomous driving, robotics, and precision agriculture, by enabling more robust and efficient object detection systems.