Semantic segmentation algorithms that can robustly segment objects across
multiple camera viewpoints are crucial for assuring navigation and safety in
emerging applications such as autonomous driving. Existing algorithms treat
each image in isolation, but autonomous vehicles often revisit the same
locations or maintain information from the immediate past. We propose a simple
yet effective method for leveraging these image priors to improve semantic
segmentation of images from sequential driving datasets. We examine several
methods to fuse these temporal scene priors, and introduce a prior fusion
network that is able to learn how to transfer this information. The prior
fusion model improves the accuracy over the non-prior baseline from 69.1% to
73.3% for dynamic classes, and from 88.2% to 89.1% for static classes. Compared
to models such as FCN-8, our prior method achieves the same accuracy with 5
times fewer parameters. We used a simple encoder decoder backbone, but this
general prior fusion method could be applied to more complex semantic
segmentation backbones. We also discuss how structured representations of
scenes in the form of a scene graph could be leveraged as priors to further
improve scene understanding.

提出了一种简单而有效的方法，通过利用与自动驾驶相关的时序数据的图像先验来改善语义分割准确性，该方法采用先验融合网络来学习先前图像的特征，最终将具体应用于提高场景理解。