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Window attention, position embeddings, and high resolution finetuning arecore concepts in the modern transformer era of computer vision. However, wefind that naively combining these near ubiquitous components can have adetrimental effect on performance. The issue is simple: interpolating positionembeddings while using window attention is wrong. We study two state-of-the-artmethods that have these three components, namely Hiera and ViTDet, and findthat both do indeed suffer from this bug. To fix it, we introduce a simpleabsolute window position embedding strategy, which solves the bug outright inHiera and allows us to increase both speed and performance of the model inViTDet. We finally combine the two to obtain HieraDet, which achieves 61.7 boxmAP on COCO, making it state-of-the-art for models that only use ImageNet-1kpretraining. This all stems from what is essentially a 3 line bug fix, which wename "absolute win".

Window Attention is Bugged: How not to Interpolate Position Embeddings