Title: Hierarchical Prompting Improves Visual Recognition On Accuracy, Data Efficiency and Explainability. Abstract: When humans try to distinguish some inherently similar visual concepts, e.g., Rosa Peace and China Rose, they may use the underlying hierarchical taxonomy to prompt the recognition. For example, given a prompt that the image belongs to the rose family, a person can narrow down the category range and thus focuses on the comparison between different roses. In this paper, we explore the hierarchical prompting for deep visual recognition (image classification, in particular) based on the prompting mechanism of the transformer. We show that the transformer can take the similar benefit by injecting the coarse-class prompts into the intermediate blocks. The resulting Transformer with Hierarchical Prompting (TransHP) is very simple and consists of three steps: 1) TransHP learns a set of prompt tokens to represent the coarse classes, 2) learns to predict the coarse class of the input image using an intermediate block, and 3) absorbs the prompt token of the predicted coarse class into the feature tokens. Consequently, the injected coarse-class prompt conditions (influences) the subsequent feature extraction and encourages better focus on the relatively subtle differences among the descendant classes. Through extensive experiments on popular image classification datasets, we show that this simple hierarchical prompting improves visual recognition on classification accuracy (e.g., improving ViT-B/16 by $+2.83\%$ ImageNet classification accuracy), training data efficiency (e.g., $+12.69\%$ improvement over the baseline under $10\%$ ImageNet training data), and model explainability.