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Chengpei Xu

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3 papers

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AAAI Conference 2026 Conference Paper

Learning 3D Occupancy from Beam Overlap in 2D Rotating mmWave Radar

Yu Du
Ruifeng Nie
Long Ma
Chengpei Xu
Yu Liu
Weimin Wang

Robust 3D perception under adverse weather is critical for autonomous systems. While mmWave Radars are inherently weather-resistant, conventional 2D rotating Radar sensors lack direct elevation resolution, limiting their 3D perception ability. Although 4D imaging radars can provide elevation information, they typically suffer from limited coverage and range. In this work, we exploit a key observation about mechanically rotating 2D mmWave Radars: in each sweep, an overlap exists between adjacent azimuth beam coverage due to the width of the main lobe, which makes the reflected intensity difference imply object materials and geometric shapes, including elevation. With this observation, we propose a method that learns 3D occupancy by disentangling bird’s-eye view (BEV) layout and elevation estimation from one frame Radar scan. Specifically, we partition one sweep into two interleaved subsets, corresponding to overlapping beam directions, and utilize them to infer coarse geometric structure through spatial differences and intensity patterns. Extensive quantitative and qualitative evaluations on two real-world datasets demonstrate that our proposed method outperforms existing baselines. The codes will be publicly available.

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TIST Journal 2026 Journal Article

LKAFormer: A Lightweight Kolmogorov-Arnold Transformer Model for Image Semantic Segmentation

Shoulin Yin
Liguo Wang
Tao Chen
Huafei Huang
Jing Gao
Jianing Zhang
Meng Liu
Peng Li

Transformer-based semantic segmentation methods have demonstrated outstanding performance by leveraging global self-attention to effectively capture long-range dependence. However, there still exist two issues in existing works: (1) Most of them utilize the full-rank weight matrix to support the self-attention mechanism and feed-forward network in modelling long-range dependence between patches/pixels, resulting in a high computational cost during both training and inference. (2) Most of them ignore information interactions between high-level semantics and low-level structures during the image resolution recovery, which leads to the performance degradation in segmenting objects with complex boundaries. To tackle these challenges, a lightweight Kolmogorov-Arnold Transformer model (LKAFormer) is proposed for the image semantic segmentation, containing a two-stream lightweight Transformer encoder and a graph feature pyramid aggregation KAN-decoder. The former constructs a hierarchical feature cross-scale fusion pipeline to obtain sufficient semantics containing comprehensive multi-scale information via setting coarse-grained and fine-grained streams with different-size patches of images. In that pipeline, feature lightweight focusing modules model complex and long-range dependence across patches/pixels to refine image semantics with less computational costs by lightweight multi-head self-attention and lightweight feed-forward network designs. The latter leverages the learnable nonlinear transformation mechanism of the Kolmogorov-Arnold Transformer architecture to adaptively capture spatial structure dependence of distinct sub-regions of images. And then, it jointly performs the intra-scale graph fusion and cross-scale graph fusion during the image resolution recovery to enhance information interactions between high-level semantics and low-level structures, which achieves the robust boundary localization and texture refinement of segmentation objects. Finally, plentiful experiments are conducted on three challenging datasets, and the results show LKAFormer sets a new baseline in the image segmentation task in comparison with 11 methods.

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AAAI Conference 2025 Conference Paper

FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning

Renqiang Luo
Huafei Huang
Ivan Lee
Chengpei Xu
Jianzhong Qi
Feng Xia

Recent studies have highlighted significant fairness issues in Graph Transformer (GT) models, particularly against subgroups defined by sensitive features. Additionally, GTs are computationally intensive and memory-demanding, limiting their application to large-scale graphs. Our experiments demonstrate that graph partitioning can enhance the fairness of GT models while reducing computational complexity. To understand this improvement, we conducted a theoretical investigation into the root causes of fairness issues in GT models. We found that the sensitive features of higher-order nodes disproportionately influence lower-order nodes, resulting in sensitive feature bias. We propose Fairness-aware scalable GT based on Graph Partitioning (FairGP), which partitions the graph to minimize the negative impact of higher-order nodes. By optimizing attention mechanisms, FairGP mitigates the bias introduced by global attention, thereby enhancing fairness. Extensive empirical evaluations on six real-world datasets validate the superior performance of FairGP in achieving fairness compared to state-of-the-art methods.

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