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The vision transformer (ViT) has achieved state-of-the-art results in variousvision tasks. It utilizes a learnable position embedding (PE) mechanism toencode the location of each image patch. However, it is presently unclear ifthis learnable PE is really necessary and what its benefits are. This paperexplores two alternative ways of encoding the location of individual patchesthat exploit prior knowledge about their spatial arrangement. One is called thesequence relationship embedding (SRE), and the other is called the circlerelationship embedding (CRE). Among them, the SRE considers all patches to bein order, and adjacent patches have the same interval distance. The CREconsiders the central patch as the center of the circle and measures thedistance of the remaining patches from the center based on the fourneighborhoods principle. Multiple concentric circles with different radiicombine different patches. Finally, we implemented these two relations on threeclassic ViTs and tested them on four popular datasets. Experiments show thatSRE and CRE can replace PE to reduce the random learnable parameters whileachieving the same performance. Combining SRE or CRE with PE gets betterperformance than only using PE.

Sequence and Circle: Exploring the Relationship Between Patches