Using Caterpillar to Nibble Small-Scale Images
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Recently, MLP-based models have become popular and attained significant performance on medium-scale datasets (e.g., ImageNet-1k). However, their direct applications to small-scale images remain limited. To address this issue, we design a new MLP-based network, namely Caterpillar, by proposing a key module of Shifted-Pillars-Concatenation (SPC) for exploiting the inductive bias of locality. SPC consists of two processes: (1) Pillars-Shift, which is to shift all pillars within an image along different directions to generate copies, and (2) Pillars-Concatenation, which is to capture the local information from discrete shift neighborhoods of the shifted copies. Extensive experiments demonstrate its strong scalability and superior performance on popular small-scale datasets, and the competitive performance on ImageNet-1K to recent state-of-the-art methods.
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