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BoxeR: Box-Attention for 2D and 3D Transformers
[article]
2022
arXiv
pre-print
In this paper, we propose a simple attention mechanism, we call box-attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map. The BoxeR computes attention weights on these boxes by considering its grid
arXiv:2111.13087v2
fatcat:jz2ars2rf5esnhi7x2ycfji22m