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@@ -5,13 +5,11 @@ By [Zizheng Pan](https://scholar.google.com.au/citations?user=w_VMopoAAAAJ&hl=en
-LITv2 Architecture
We introduce LITv2, a simple and effective ViT which performs favourably against the existing state-of-the-art methods across a spectrum of different model sizes with faster speed.
-HiLo Attention
The core of LITv2: **HiLo attention**. HiLo is inspired by the insight that high frequencies in an image capture local fine details and low frequencies focus on global structures, whereas a multi-head self-attention layer neglects the characteristic of different frequencies. Therefore, we propose to disentangle the high/low frequency patterns in an attention layer by separating the heads into two groups, where one group encodes high frequencies via self-attention within each local window, and another group performs the attention to model the global relationship between the average-pooled low-frequency keys from each window and each query position in the input feature map.