Dream-Box：用于分布外检测的逐对象离群值生成
Dream-Box: Object-wise Outlier Generation for Out-of-Distribution Detection
AI总结：
研究提出Dream-Box方法，利用扩散模型在像素空间生成逐对象离群值以训练目标检测器，实现OOD检测，性能与传统方法相当且首次提供离群对象可视化。
摘要：
深度神经网络在训练集和测试集来自相同分布的任务上展现了出色的泛化能力。然而，分布外（OOD）检测仍是一项具有挑战性的任务，近年来受到了广泛关注。具体而言，OOD检测是指检测不属于训练分布的实例，同时在分布内任务（如分类或目标检测）上保持良好性能。近期工作集中于生成合成离群值并利用它们训练离群值检测器，通常比传统OOD方法取得更好的OOD检测效果。在此方面，离群值可以在特征空间或像素空间中生成。特征空间驱动的方法在分类和目标检测任务上均表现出强劲的性能，代价是训练离群值的可视化仍然未知，使得对OOD失败模式的进一步分析具有挑战性。另一方面，由扩散模型实现的像素空间离群值生成技术已用于图像分类，提供了改进的OOD检测性能和离群值可视化，尽管其向目标检测任务的适配尚未被探索。因此，我们引入Dream-Box，这是一种为OOD检测在像素空间中提供逐对象离群值生成链接的方法。具体而言，我们使用扩散模型生成逐对象离群值，用于训练目标检测器以执行分布内任务和OOD检测。我们的方法取得了与以往传统方法相当的性能，同时是首个为生成的OOD对象提供具体可视化的技术。
英文摘要：
Deep neural networks have demonstrated great generalization capabilities for tasks whose training and test sets are drawn from the same distribution. Nevertheless, out-of-distribution (OOD) detection remains a challenging task that has received significant attention in recent years. Specifically, OOD detection refers to the detection of instances that do not belong to the training distribution, while still having good performance on the in-distribution task (e.g., classification or object detection). Recent work has focused on generating synthetic outliers and using them to train an outlier detector, generally achieving improved OOD detection than traditional OOD methods. In this regard, outliers can be generated either in feature or pixel space. Feature space driven methods have shown strong performance on both the classification and object detection tasks, at the expense that the visualization of training outliers remains unknown, making further analysis on OOD failure modes challenging. On the other hand, pixel space outlier generation techniques enabled by diffusion models have been used for image classification using, providing improved OOD detection performance and outlier visualization, although their adaption to the object detection task is as yet unexplored. We therefore introduce Dream-Box, a method that provides a link to object-wise outlier generation in the pixel space for OOD detection. Specifically, we use diffusion models to generate object-wise outliers that are used to train an object detector for an in-distribution task and OOD detection. Our method achieves comparable performance to previous traditional methods while being the first technique to provide concrete visualization of generated OOD objects.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
