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Scene segmentation in images is a fundamental yet challenging problem invisual content understanding, which is to learn a model to assign every imagepixel to a categorical label. One of the challenges for this learning task isto consider the spatial and semantic relationships to obtain descriptivefeature representations, so learning the feature maps from multiple scales is acommon practice in scene segmentation. In this paper, we explore the effectiveuse of self-attention within multi-scale image windows to learn descriptivevisual features, then propose three different strategies to aggregate thesefeature maps to decode the feature representation for dense prediction. Ourdesign is based on the recently proposed Swin Transformer models, which totallydiscards convolution operations. With the simple yet effective multi-scalefeature learning and aggregation, our models achieve very promising performanceon four public scene segmentation datasets, PASCAL VOC2012, COCO-Stuff 10K,ADE20K and Cityscapes.

Self-attention on Multi-Shifted Windows for Scene Segmentation