  Modeling spatial relationship in the data remains critical across many
different tasks, such as image classification, semantic segmentation and
protein structure understanding. Previous works often use a unified solution
like relative positional encoding. However, there exists different kinds of
spatial relations, including short-range, medium-range and long-range
relations, and modeling them separately can better capture the focus of
different tasks on the multi-range relations (e.g., short-range relations can
be important in instance segmentation, while long-range relations should be
upweighted for semantic segmentation). In this work, we introduce the EurNet
for Efficient multi-range relational modeling. EurNet constructs the
multi-relational graph, where each type of edge corresponds to short-, medium-
or long-range spatial interactions. In the constructed graph, EurNet adopts a
novel modeling layer, called gated relational message passing (GRMP), to
propagate multi-relational information across the data. GRMP captures multiple
relations within the data with little extra computational cost. We study
EurNets in two important domains for image and protein structure modeling.
Extensive experiments on ImageNet classification, COCO object detection and
ADE20K semantic segmentation verify the gains of EurNet over the previous SoTA
FocalNet. On the EC and GO protein function prediction benchmarks, EurNet
consistently surpasses the previous SoTA GearNet. Our results demonstrate the
strength of EurNets on modeling spatial multi-relational data from various
domains. The implementations of EurNet for image modeling are available at
https://github.com/hirl-team/EurNet-Image . The implementations for other
applied domains/tasks will be released soon.
