  Buddha statues, as a symbol of many religions, have significant cultural
implications that are crucial for understanding the culture and history of
different regions, and the recognition of Buddha statues is therefore the
pivotal link in the field of Buddha study. However, the Buddha statue
recognition requires extensive time and effort from knowledgeable
professionals, making it a costly task to perform. Convolution neural networks
(CNNs) are inherently efficient at processing visual information, but CNNs
alone are likely to make inaccurate classification decisions when subjected to
the class imbalance problem. Therefore, this paper proposes an end-to-end
automatic Buddha statue recognition model based on saliency map sampling. The
proposed Grid-Wise Local Self-Attention Module (GLSA) provides extra salient
features which can serve to enrich the dataset and allow CNNs to observe in a
much more comprehensive way. Eventually, our model is evaluated on a Buddha
dataset collected with the aid of Buddha experts and outperforms
state-of-the-art networks in terms of Top-1 accuracy by 4.63\% on average,
while only marginally increasing MUL-ADD.
