Open Set Object

Open-set object detection (OSOD) addresses the challenge of identifying both known and unknown objects within a visual scene, a crucial capability for robust real-world applications like autonomous driving and robotics. Current research focuses on developing models that effectively distinguish between known and unknown classes, often employing transformer-based architectures and techniques like contrastive learning, visual prompting, and pseudo-labeling to improve performance, particularly in low-data or imbalanced scenarios. These advancements are significant because they enable systems to handle unforeseen objects and adapt to dynamic environments, improving the reliability and safety of AI systems in various domains.