An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has ...

The feature maps from the Swin Transformer module (Liu et al., 2021) represent global contextual information. Through its self-attention mechanism, it is evident how the network captures long-range ...

Timely acquiring the earthquake-induced damage of buildings is crucial for emergency assessment and post-disaster rescue. Optical remote sensing is a typical method for obtaining seismic data due to ...

Semantic segmentation of remote sensing images plays a crucial role in a wide variety of practical applications, including land cover mapping, environmental protection, and economic assessment. In the ...

SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several ...

Nevertheless, the optimum output of the cycle-consistency model may not be unique. 2) They are still deficient in capturing the global features and modeling long-distance interactions, which are ...

Swin Transformer, a Transformer-based general-purpose vision architecture, was further evolved to address challenges specific to large vision models. As a result, Swin Transformer is capable of ...

Can you explain how the cyclic shift changes the feature map, and what position of the tokens is masked during the calculation of the attention? As in your paper's figure , it's too abstract for me.