  As bird's-eye-view (BEV) semantic segmentation is simple-to-visualize and
easy-to-handle, it has been applied in autonomous driving to provide the
surrounding information to downstream tasks. Inferring BEV semantic
segmentation conditioned on multi-camera-view images is a popular scheme in the
community as cheap devices and real-time processing. The recent work
implemented this task by learning the content and position relationship via the
vision Transformer (ViT). However, the quadratic complexity of ViT confines the
relationship learning only in the latent layer, leaving the scale gap to impede
the representation of fine-grained objects. And their plain fusion method of
multi-view features does not conform to the information absorption intention in
representing BEV features. To tackle these issues, we propose a novel
cross-scale hierarchical Transformer with correspondence-augmented attention
for semantic segmentation inferring. Specifically, we devise a hierarchical
framework to refine the BEV feature representation, where the last size is only
half of the final segmentation. To save the computation increase caused by this
hierarchical framework, we exploit the cross-scale Transformer to learn feature
relationships in a reversed-aligning way, and leverage the residual connection
of BEV features to facilitate information transmission between scales. We
propose correspondence-augmented attention to distinguish conducive and
inconducive correspondences. It is implemented in a simple yet effective way,
amplifying attention scores before the Softmax operation, so that the
position-view-related and the position-view-disrelated attention scores are
highlighted and suppressed. Extensive experiments demonstrate that our method
has state-of-the-art performance in inferring BEV semantic segmentation
conditioned on multi-camera-view images.
