关于AxialAttention中kernel_size的问题 about axial-deeplab HOT 1 CLOSED

1. In the paper we only explored using axial-self-attention as a backbone, but one can certainly explore extending it to non-self-attention or cross-attention, e.g. attending from a 2D map to another 2D map.
2. The current code only supports global attention and the same train and eval resolution. But in general, axial-attention is not limited to different input resolutions: one should use local axial-attention with a fixed span (e.g. 65) for that. In the paper, we used span = (65x65) for the main panoptic segmentation results, and this allows us to do multi-scale inference with different input resolutions.

from axial-deeplab.