Recent works have widely explored the contextual dependencies to achieve more
accurate segmentation results. However, most approaches rarely distinguish
different types of contextual dependencies, which may pollute the scene
understanding. In this work, we directly supervise the feature aggregation to
distinguish the intra-class and inter-class context clearly. Specifically, we
develop a Context Prior with the supervision of the Affinity Loss. Given an
input image and corresponding ground truth, Affinity Loss constructs an ideal
affinity map to supervise the learning of Context Prior. The learned Context
Prior extracts the pixels belonging to the same category, while the reversed
prior focuses on the pixels of different classes. Embedded into a conventional
deep CNN, the proposed Context Prior Layer can selectively capture the
intra-class and inter-class contextual dependencies, leading to robust feature
representation. To validate the effectiveness, we design an effective Context
Prior Network (CPNet). Extensive quantitative and qualitative evaluations
demonstrate that the proposed model performs favorably against state-of-the-art
semantic segmentation approaches. More specifically, our algorithm achieves
46.3% mIoU on ADE20K, 53.9% mIoU on PASCAL-Context, and 81.3% mIoU on
Cityscapes. Code is available at this https URL

本文研究利用上下文依赖关系解决图像分割问题，提出了一种 Context Prior 神经网络，在特征集成时利用 Affinity Loss 监管不同类别的场景，从而提高了分割结果的准确性。实验证明，该网络在 ADE20K，PASCAL-Context 和 Cityscapes 的数据集中取得了较优的分割效果。

场景分割的上下文先验

Context Prior for Scene Segmentation

Knowledge of 3D properties of objects is a necessity in order to build
effective computer vision systems. However, lack of large scale 3D datasets can
be a major constraint for data-driven approaches in learning such properties.
We consider the task of single image 3D point cloud reconstruction, and aim to
utilize multiple foreground masks as our supervisory data to alleviate the need
for large scale 3D datasets. A novel differentiable projection module, called
'CAPNet', is introduced to obtain such 2D masks from a predicted 3D point
cloud. The key idea is to model the projections as a continuous approximation
of the points in the point cloud. To overcome the challenges of sparse
projection maps, we propose a loss formulation termed 'affinity loss' to
generate outlier-free reconstructions. We significantly outperform the existing
projection based approaches on a large-scale synthetic dataset. We show the
utility and generalizability of such a 2D supervised approach through
experiments on a real-world dataset, where lack of 3D data can be a serious
concern. To further enhance the reconstructions, we also propose a test stage
optimization procedure to obtain reconstructions that display high
correspondence with the observed input image.

提出了一种名为 CAPNet 的不可分辨投影模块，采用多个前景掩膜作为监督数据，通过连续逼近点云点来建立投影模型，通过 'affinity loss' 损失公式规避了稀疏投影问题，显著优于现有基于投影的方法，可在缺乏大量 3D 数据的情况下构建高质量 3D 重建。