Real World Detection

Real-world detection research focuses on improving the robustness and adaptability of object detection systems to handle the complexities of real-world scenarios. Current efforts concentrate on addressing challenges like few-shot learning for new object classes, mitigating adversarial attacks, and enabling open-set detection and discovery of unknown objects, often leveraging techniques like knowledge distillation, prompt learning, and multi-modal queries within various model architectures (e.g., YOLO, CLIP-based models). These advancements are crucial for building more reliable and versatile detection systems applicable to diverse fields such as autonomous driving, medical image analysis, and information verification, ultimately enhancing the safety and efficiency of numerous applications.