2023 article
LEARNING SPATIALLY-ADAPTIVE SQUEEZE-EXCITATION NETWORKS FOR FEW SHOT IMAGE SYNTHESIS
2023 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP, pp. 2855–2859.
author keywords: data-specificity; spatially-adaptive; attention; low-shot
TL;DR:
A spatially-adaptive squeeze-excitation module for image synthesis task that is tested in low-shot image generative learning task, and shows better performance than prior arts.
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Added: March 11, 2024
Learning light-weight yet expressive deep networks for image synthesis is a challenging problem. Inspired by a recent observation that it is the data-specificity that makes the multi-head self-attention (MHSA) in the Transformer model so powerful, this paper proposes to extend the widely adopted light-weight Squeeze-Excitation (SE) module to be spatially-adaptive to reinforce its data specificity, as a convolutional alternative of the MHSA, while retaining the efficiency of SE and the inductive bias of convolution. It proposes a spatially-adaptive squeeze-excitation (SASE) module for image synthesis task.SASE is tested in low-shot image generative learning task, and shows better performance than prior arts.