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Xiaowei Li

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11 papers
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11

IROS Conference 2025 Conference Paper

AirSwarm: Enabling Cost-Effective Multi-UAV Research with COTS drones

  • Xiaowei Li
  • Kuan Xu
  • Fen Liu
  • Ruofei Bai
  • Shenghai Yuan 0001
  • Lihua Xie 0001

Traditional unmanned aerial vehicle (UAV) swarm missions rely heavily on expensive custom-made drones with onboard perception or external positioning systems, limiting their widespread adoption in research and education. To address this issue, we propose AirSwarm. AirSwarm democratizes multi-drone coordination using low-cost commercially available drones such as Tello or Anafi, enabling affordable swarm aerial robotics research and education. Key innovations include a hierarchical control architecture for reliable multi-UAV coordination, an infrastructure-free visual SLAM system for precise localization without external motion capture, and a ROS-based software framework for simplified swarm development. Experiments demonstrate cm-level tracking accuracy, low-latency control, communication failure resistance, formation flight, and trajectory tracking. By reducing financial and technical barriers, AirSwarm makes multi-robot education and research more accessible. The complete instructions and open source code will be available at https://github.com/vvEverett/tello_ros.

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AAAI Conference 2025 Conference Paper

TGLsta: Low-resource Textual Graph Learning with Semantic and Topological Awareness via LLMs

  • Qin Zhang
  • Xiaowei Li
  • Ziqi Liu
  • Xiaochen Fan
  • Xiaojun Chen
  • Shirui Pan

Textual Graphs (TGs) present a graph-based representation of textual data and find wide applications in real-world scenarios, such as citation networks, knowledge graphs, and social networks. While the traditional "pre-train, fine-tune" framework effectively addresses tasks requiring abundant labeled data, it falls short in scenarios with limited resource or zero-shot learning capabilities, particularly in low-resource textual graph node classification. Additionally, prevalent approaches that convert text nodes into shallow or manually engineered features fail to capture the rich semantic nuances within the text. The conventional methods often neglect the fusion of semantic and topological information, resulting in suboptimal model learning. To overcome these challenges, we proposed a novel method of low-resource textual graph node classification based on large language models, i.e., Textual graph learning with semantic and topological awareness (TGLsta), which comprehensively explores the semantic information, near neighborhood information, and the topology information in textual graphs, where these components are the most important information source contained in textual graphs. Graph prompt tuning for both zero- and few-shot textual graph node classification is further introduced.

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IJCAI Conference 2024 Conference Paper

CONC: Complex-noise-resistant Open-set Node Classification with Adaptive Noise Detection

  • Qin Zhang
  • Jiexin Lu
  • Xiaowei Li
  • Huisi Wu
  • Shirui Pan
  • Junyang Chen

As a popular task in graph learning, node classification seeks to assign labels to nodes, taking into account both their features and connections. However, an important challenge for its application in real-world scenarios is the presence of newly-emerged out-of-distribution samples and noisy samples, which affect the quality and robustness of learned classifiers. Out-of-distribution (OOD) samples are often found in both the training and testing phases. Such samples don’t belong to any known categories. These OOD samples are considered as outliers (OOD noise) when they appear during training, and are recognized as open-set samples during the testing. Meanwhile, in-distribution (IND) noisy data, i. e. , known class samples with wrong labels, are also prevalent and inevitably degrade a model’s performance. The challenge of open-set learning with complex IND and OOD noise remains largely unexplored, particularly when dealing with non-IID graph data. To address these challenges, this paper introduces a novel complex-noise-resistant open-set node classification approach, designed for open-set graph data containing both IND and OOD noisy nodes. Specifically, a trustworthiness learner is adopted to learn the trustworthiness rates of the feature and label for each node while a decoder and an open-set classifier are trained to reconstruct the structure of a node and to predict its category simultaneously with the guidance of node trustworthiness. The experimental results demonstrate the superiority of our method.

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AAAI Conference 2024 Conference Paper

ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning

  • Qin Zhang
  • Xiaowei Li
  • Jiexin Lu
  • Liping Qiu
  • Shirui Pan
  • Xiaojun Chen
  • Junyang Chen

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are samples that do not belong to any known classes. They are outliers if they occur in training (OOD noise), and open-set samples if they occur in testing. IND noise are training samples which are assigned incorrect labels. The existence of IND noise and OOD noise is prevalent, which usually cause the ambiguity problem, including the intra-class variety problem and the inter-class confusion problem. Thus, to explore robust open-set learning methods is necessary and difficult, and it becomes even more difficult for non-IID graph data. To this end, we propose a unified framework named ROG_PL to achieve robust open-set learning on complex noisy graph data, by introducing prototype learning. In specific, ROG_PL consists of two modules, i.e., denoising via label propagation and open-set prototype learning via regions. The first module corrects noisy labels through similarity-based label propagation and removes low-confidence samples, to solve the intra-class variety problem caused by noise. The second module learns open-set prototypes for each known class via non-overlapped regions and remains both interior and border prototypes to remedy the inter-class confusion problem. The two modules are iteratively updated under the constraints of classification loss and prototype diversity loss. To the best of our knowledge, the proposed ROG_PL is the first robust open-set node classification method for graph data with complex noise. Experimental evaluations of ROG_PL on several benchmark graph datasets demonstrate that it has good performance.

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JBHI Journal 2023 Journal Article

Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals

  • Shuting Sun
  • Huayu Chen
  • Gang Luo
  • Chang Yan
  • Qunxi Dong
  • Xuexiao Shao
  • Xiaowei Li
  • Bin Hu

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

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NeurIPS Conference 2021 Conference Paper

PLUGIn: A simple algorithm for inverting generative models with recovery guarantees

  • Babhru Joshi
  • Xiaowei Li
  • Yaniv Plan
  • Ozgur Yilmaz

We consider the problem of recovering an unknown latent code vector under a known generative model. For a $d$-layer deep generative network $\mathcal{G}: \mathbb{R}^{n_0}\rightarrow \mathbb{R}^{n_d}$ with ReLU activation functions, let the observation be $\mathcal{G}(x)+\epsilon$ where $\epsilon$ is noise. We introduce a simple novel algorithm, Partially Linearized Update for Generative Inversion (PLUGIn), to estimate $x$ (and thus $\mathcal{G}(x)$). We prove that, when weights are Gaussian and layer widths $n_i \gtrsim 5^i n_0$ (up to log factors), the algorithm converges geometrically to a neighbourhood of $x$ with high probability. Note the inequality on layer widths allows $n_i>n_{i+1}$ when $i\geq 1$. To our knowledge, this is the first such result for networks with some contractive layers. After a sufficient number of iterations, the estimation errors for both $x$ and $\mathcal{G}(x)$ are at most in the order of $\sqrt{4^dn_0/n_d} \|\epsilon\|$. Thus, the algorithm can denoise when the expansion ratio $n_d/n_0$ is large. Numerical experiments on synthetic data and real data are provided to validate our theoretical results and to illustrate that the algorithm can effectively remove artifacts in an image.

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NeurIPS Conference 2018 Conference Paper

See and Think: Disentangling Semantic Scene Completion

  • Shice Liu
  • Yu Hu
  • Yiming Zeng
  • Qiankun Tang
  • Beibei Jin
  • Yinhe Han
  • Xiaowei Li

Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled framework, sequentially carrying out 2D semantic segmentation, 2D-3D reprojection and 3D semantic scene completion. This three-stage framework has three advantages: (1) explicit semantic segmentation significantly boosts performance; (2) flexible fusion ways of sensor data bring good extensibility; (3) progress in any subtask will promote the holistic performance. Experimental results show that regardless of inputing a single depth or RGB-D, our framework can generate high-quality semantic scene completion, and outperforms state-of-the-art approaches on both synthetic and real datasets.

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