🤖 AI Summary
Existing Plot2API approaches struggle with hand-drawn charts from non-expert users, primarily due to domain gaps and the absence of tailored training data. To address this limitation, this work introduces HDpy-13, the first dedicated dataset of hand-drawn charts, and proposes a lightweight Plot-Adapter architecture. The design leverages independent adapter modules that integrate CNN-based local feature enhancement with shared projection matrices, enabling efficient graphical API recommendation across multilingual and multidomain scenarios. By decoupling task-specific adaptation from the base model, the approach substantially reduces both parameter count and computational overhead while maintaining high recommendation accuracy. Experimental results demonstrate the method’s effectiveness and scalability under the paradigm of parameter-efficient fine-tuning (PEFT), offering a practical solution for real-world deployment in diverse user environments.
📝 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 considerable strain of parameter growth and computational resource costs arising from multi-domain and multi-language challenges in Plot2API, we propose Plot-Adapter that allows for the training and storage of separate adapters rather than requiring an entire model for each language and domain. In particular, Plot-Adapter incorporates a lightweight CNN block to improve the ability to capture local features and implements projection matrix sharing to reduce the number of fine-tuning parameters further. Experimental results demonstrate both the effectiveness of HDpy-13 and the efficiency of Plot-Adapter.