From Pen to Pixel: Translating Hand-Drawn Plots into Graphical APIs via a Novel Benchmark and Efficient Adapter
Zhenghao Xu (1), Mengning Yang (1) ((1) School of Big Data, Software Engineering, Chongqing University, Chongqing, China)

TL;DR
This paper introduces HDpy-13, a new dataset of hand-drawn plots, and Plot-Adapter, a lightweight model component, to improve graphical API recommendation for hand-drawn plots, addressing domain gaps and resource costs.
Contribution
The paper presents a novel hand-drawn plot dataset and an efficient adapter-based model to enhance API recommendation accuracy and reduce computational costs.
Findings
HDpy-13 improves API recommendation for hand-drawn plots.
Plot-Adapter reduces model complexity and training resources.
Experimental results confirm the effectiveness of both HDpy-13 and Plot-Adapter.
Abstract
As plots play a critical role in modern data visualization and analysis, Plot2API is launched to help non-experts and beginners create their desired plots by directly recommending graphical APIs from reference plot images by neural networks. However, previous works on Plot2API have primarily focused on the recommendation for standard plot images, while overlooking the hand-drawn plot images that are more accessible to non-experts and beginners. To make matters worse, both Plot2API models trained on standard plot images and powerful multi-modal large language models struggle to effectively recommend APIs for hand-drawn plot images due to the domain gap and lack of expertise. To facilitate non-experts and beginners, we introduce a hand-drawn plot dataset named HDpy-13 to improve the performance of graphical API recommendations for hand-drawn plot images. Additionally, to alleviate the…
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