Image Model Blocks

Axial Attention

Introduced by Ho et al. in Axial Attention in Multidimensional Transformers

Axial attention is a simple generalization of self-attention that naturally aligns with the multiple dimensions of the tensors in both the encoding and the decoding settings. The proposed structure of the layers allows for the vast majority of the context to be computed in parallel during decoding without introducing any independence assumptions. It serves as the basic building block for developing self-attention-based autoregressive models for high-dimensional data tensors, e.g., Axial Transformers.

Source: Axial Attention in Multidimensional Transformers

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