Distribution Object

Out-of-distribution (OOD) object detection focuses on improving the robustness of object detectors when encountering objects unseen during training. Current research emphasizes developing methods to synthesize OOD data using generative models or self-supervised learning, and leveraging features from existing object detectors (like those based on convolutional neural networks) to better distinguish between in-distribution and OOD objects. These advancements are crucial for enhancing the reliability and safety of object detection systems in real-world applications, such as autonomous driving and robotics, where encountering unexpected objects is inevitable. Improved OOD detection is achieved through various techniques including Bayesian uncertainty estimation and analysis of neuron activation patterns.