论文标题
通过贝叶斯模型通过外套外和分发概括的分段分割
Amodal Segmentation through Out-of-Task and Out-of-Distribution Generalization with a Bayesian Model
论文作者
论文摘要
Amodal完成是人类易于执行的视觉任务，但对于计算机视觉算法来说很难。目的是细分那些被遮挡的对象边界，因此是看不见的。对于深层神经网络而言，此任务尤其具有挑战性，因为数据难以获得和注释。因此，我们将Amodal分割作为任务外和分布外的概括问题。具体而言，我们用神经网络特征的贝叶斯生成模型代替了神经网络中完全连接的分类器。该模型仅使用边界框注释和类标签从非封闭图像训练，但应用于对对象进行分割的概括并概括分布外的对象。我们演示了这样的贝叶斯模型如何在训练任务标签中自然概括，因为他们在对象的背景上下文和形状进行了建模。此外，通过利用离群过程，贝叶斯模型可以进一步概括分布以部分遮挡的对象并预测其氨基对象边界。我们的算法优于替代方法，这些方法使用相同的监督，甚至超过了在训练过程中使用带注释的Amodal分割的方法，即闭塞量很大。代码可在https://github.com/yihongsun/bayesian-amodal上公开获取。
Amodal completion is a visual task that humans perform easily but which is difficult for computer vision algorithms. The aim is to segment those object boundaries which are occluded and hence invisible. This task is particularly challenging for deep neural networks because data is difficult to obtain and annotate. Therefore, we formulate amodal segmentation as an out-of-task and out-of-distribution generalization problem. Specifically, we replace the fully connected classifier in neural networks with a Bayesian generative model of the neural network features. The model is trained from non-occluded images using bounding box annotations and class labels only, but is applied to generalize out-of-task to object segmentation and to generalize out-of-distribution to segment occluded objects. We demonstrate how such Bayesian models can naturally generalize beyond the training task labels when they learn a prior that models the object's background context and shape. Moreover, by leveraging an outlier process, Bayesian models can further generalize out-of-distribution to segment partially occluded objects and to predict their amodal object boundaries. Our algorithm outperforms alternative methods that use the same supervision by a large margin, and even outperforms methods where annotated amodal segmentations are used during training, when the amount of occlusion is large. Code is publicly available at https://github.com/YihongSun/Bayesian-Amodal.
