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• W3011199263 abstract "Convolution exploits locality for efficiency at a cost of missing long range context. Self-attention has been adopted to augment CNNs with non-local interactions. Recent works prove it possible to stack self-attention layers to obtain a fully attentional network by restricting the attention to a local region. In this paper, we attempt to remove this constraint by factorizing 2D self-attention into two 1D self-attentions. This reduces computation complexity and allows performing attention within a larger or even global region. In companion, we also propose a position-sensitive self-attention design. Combining both yields our position-sensitive axial-attention layer, a novel building block that one could stack to form axial-attention models for image classification and dense prediction. We demonstrate the effectiveness of our model on four large-scale datasets. In particular, our model outperforms all existing stand-alone self-attention models on ImageNet. Our Axial-DeepLab improves 2.8% PQ over bottom-up state-of-the-art on COCO test-dev. This previous state-of-the-art is attained by our small variant that is 3.8x parameter-efficient and 27x computation-efficient. Axial-DeepLab also achieves state-of-the-art results on Mapillary Vistas and Cityscapes." @default.
• W3011199263 created "2020-03-23" @default.
• W3011199263 creator A5006455453 @default.
• W3011199263 creator A5028001282 @default.
• W3011199263 creator A5030402556 @default.
• W3011199263 creator A5034551921 @default.
• W3011199263 creator A5086706224 @default.
• W3011199263 creator A5090034335 @default.
• W3011199263 date "2020-03-17" @default.
• W3011199263 modified "2023-10-03" @default.
• W3011199263 title "Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation" @default.
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