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Yanpeng Li

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4 papers

2 author rows

AAAI Conference 2026 System Paper

GeoProblem Factory: A Visual Interaction System for Solvable and Controllable Geometric Problem Generation by Leveraging Symbolic Deduction Engine

Zhuoxuan Jiang
Yanpeng Li
Tianyang Zhang
Jing Chen
Yong Li
Mo Guang
Wen Si
Shaohua Zhang

We propose a novel system, GeoProblem Factory, designed to effectively generate high-quality geometry problems for intelligent education. The system enables to efficiently produce batches of geometry problems for teachers and students, either to save time and manual effort or to support personalized learning. Generating geometry problems is particularly challenging, as it requires ensuring both solvability and controllability from a pedagogical perspective. To address these issues, we adopt a state-of-the-art pipeline method based on a symbolic deduction engine and develop a visual interaction demo. This demo allows users to easily refine the generated problems through visual operations. It provides two modes for inputting controllable information: specifying knowledge points or supplying a reference problem. Moreover, the system can automatically generate a preliminary geometric diagram corresponding to each problem for further refinement. Through human–machine interaction, the system can more efficiently produce high-quality geometry problems than ever.

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NeurIPS Conference 2025 Conference Paper

ArchCAD-400K: A Large-Scale CAD drawings Dataset and New Baseline for Panoptic Symbol Spotting

Ruifeng Luo
Zhengjie Liu
Tianxiao Cheng
Jie Wang
Tongjie Wang
Fei Cheng
Fu Chai
Yanpeng Li

Recognizing symbols in architectural CAD drawings is critical for various advanced engineering applications. In this paper, we propose a novel CAD data annotation engine that leverages intrinsic attributes from systematically archived CAD drawings to automatically generate high-quality annotations, thus significantly reducing manual labeling efforts. Utilizing this engine, we construct ArchCAD-400K, a large-scale CAD dataset consisting of 413, 062 chunks from 5538 highly standardized drawings, making it over 26 times larger than the largest existing CAD dataset. ArchCAD-400K boasts an extended drawing diversity and broader categories, offering line-grained annotations. Furthermore, we present a new baseline model for panoptic symbol spotting, termed Dual-Pathway Symbol Spotter (DPSS). It incorporates an adaptive fusion module to enhance primitive features with complementary image features, achieving state-of-the-art performance and enhanced robustness. Extensive experiments validate the effectiveness of DPSS, demonstrating the value of ArchCAD-400K and its potential to drive innovation in architectural design and construction.

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IROS Conference 2024 Conference Paper

Lightweight Fisheye Object Detection Network with Transformer-based Feature Enhancement for Autonomous Driving

Hu Cao
Yanpeng Li
Yinlong Liu
Xinyi Li
Guang Chen 0001
Alois C. Knoll

Fisheye cameras, offering a wide field of view (FOV) of 360 ◦, are extensively employed for surround-view perception in autonomous driving. Compared with the object detection on the standard images, it lacks studies for fisheye images. Moreover, efficient perception is crucial for autonomous vehicles with limited computational capability. In this work, we introduce a lightweight fisheye object detection network with transformer-based feature enhancement for autonomous driving. Specifically, we leverage ShuffleNet V2 as a feature extraction network to reduce computation complexity and develop a transformer-based feature enhancement module (TFEM) to integrate multi-level features. Notably, we observe that data augmentation methods like mix-up and mosaic, effective on standard images, do not yield positive results on fisheye images. The results on the WoodScape dataset demonstrate that our method can achieve better performance with fewer parameters and floating-point operations per second (FLOPs). Extending our evaluation to the Microsoft Common Objects in Context (MS COCO) dataset shows that the proposed method has excellent generalization capability.

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AIIM Journal 2011 Journal Article

Multiple kernel learning in protein–protein interaction extraction from biomedical literature

Zhihao Yang
Nan Tang
Xiao Zhang
Hongfei Lin
Yanpeng Li
Zhiwei Yang

Objective Knowledge about protein–protein interactions (PPIs) unveils the molecular mechanisms of biological processes. The volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database administrators, responsible for content input and maintenance to detect and manually update protein interaction information. The objective of this work is to develop an effective approach to automatic extraction of PPI information from biomedical literature. Methods and materials We present a weighted multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, graph and part-of-speech (POS) path. In particular, we extend the shortest path-enclosed tree (SPT) and dependency path tree to capture richer contextual information. Results Our experimental results show that the combination of SPT and dependency path tree extensions contributes to the improvement of performance by almost 0. 7 percentage units in F-score and 2 percentage units in area under the receiver operating characteristics curve (AUC). Combining two or more appropriately weighed individual will further improve the performance. Both on the individual corpus and cross-corpus evaluation our combined kernel can achieve state-of-the-art performance with respect to comparable evaluations, with 64. 41% F-score and 88. 46% AUC on the AImed corpus. Conclusions As different kernels calculate the similarity between two sentences from different aspects. Our combined kernel can reduce the risk of missing important features. More specifically, we use a weighted linear combination of individual kernels instead of assigning the same weight to each individual kernel, thus allowing the introduction of each kernel to incrementally contribute to the performance improvement. In addition, SPT and dependency path tree extensions can improve the performance by including richer context information.

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