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Swin Transformer - GitHub It is basically a hierarchical Transformer whose representation is computed with shifted windows The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection
Swin Transformer - GeeksforGeeks Unlike standard vision transformers which use global attention, Swin Transformer introduces a "shifted window" technique This allows neighboring windows to interact with each other in subsequent layers, efficiently capturing both local and global features in an image
⛵ Paper Digest | Swin Transformer - Hierarchical Vision Transformer . . . Furthermore, Swin Transformer constructs a hierarchical representation by starting from small-sized patches and gradually merging neighboring patches in deeper Transformer layers These merits make Swin Transformer suitable as a general purpose backbone for various vision tasks
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows For example, in the last layer of figure (a) below, the whole picture is sliced into 16 windows, and each window have 16 4×4 pixels patches The model views a window as a sequence and calculate the self-attention scores inside the window
ICCV 2021 Open Access Repository To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection