WaveNets: Wavelet Channel Attention Networks. (arXiv:2211.02695v1 [cs.CV])

Channel Attention reigns supreme as an effective technique in the field of
computer vision. However, the proposed channel attention by SENet suffers from
information loss in feature learning caused by the use of Global Average
Pooling (GAP) to represent channels as scalars. Thus, designing effective
channel attention mechanisms requires finding a solution to enhance features
preservation in modeling channel inter-dependencies. In this work, we utilize
Wavelet transform compression as a solution to the channel representation
problem. We first test wavelet …

arxiv attention networks wavelet