Lightweight transformer image feature extraction network

In recent years, the image feature extraction method based on Transformer has become a research hotspot. However, when using Transformer for image feature extraction, the model's complexity increases quadratically with the number of tokens entered. The quadratic complexity prevents vision transformer-based backbone networks from modelling high-resolution images and is computationally expensive. To address this issue, this study proposes two approaches to speed up Transformer models. Firstly, the self-attention mechanism's quadratic complexity is reduced to linear, enhancing the model's internal processing speed. Next, a parameter-less lightweight pruning method is introduced, which adaptively samples input…