  Real world images often have highly imbalanced content density. Some areas
are very uniform, e.g., large patches of blue sky, while other areas are
scattered with many small objects. Yet, the commonly used successive grid
downsampling strategy in convolutional deep networks treats all areas equally.
Hence, small objects are represented in very few spatial locations, leading to
worse results in tasks such as segmentation. Intuitively, retaining more pixels
representing small objects during downsampling helps to preserve important
information. To achieve this, we propose AutoFocusFormer (AFF), a
local-attention transformer image recognition backbone, which performs adaptive
downsampling by learning to retain the most important pixels for the task.
Since adaptive downsampling generates a set of pixels irregularly distributed
on the image plane, we abandon the classic grid structure. Instead, we develop
a novel point-based local attention block, facilitated by a balanced clustering
module and a learnable neighborhood merging module, which yields
representations for our point-based versions of state-of-the-art segmentation
heads. Experiments show that our AutoFocusFormer (AFF) improves significantly
over baseline models of similar sizes.
