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Yukun Du

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4 papers

1 author row

AAAI Conference 2026 Conference Paper

Meta-Black-Box Optimization with Bi-Space Landscape Analysis and Dual-Control Mechanism for SAEA

Yukun Du
Haiyue Yu
Xiaotong Xie
Yan Zheng
Lixin Zhan
Yudong Du
Chongshuang Hu
Boxuan Wang

Surrogate-Assisted Evolutionary Algorithms (SAEAs) are widely used for expensive Black-Box Optimization. However, their reliance on rigid, manually designed components such as infill criteria and evolutionary strategies during the search process limits their flexibility across tasks. To address these limitations, we propose Dual-Control Bi-Space Surrogate-Assisted Evolutionary Algorithm (DB-SAEA), a Meta-Black-Box Optimization (MetaBBO) framework tailored for multi-objective problems. DB-SAEA learns a meta-policy that jointly regulates candidate generation and infill criterion selection, enabling dual control. The bi-space Exploratory Landscape Analysis (ELA) module in DB-SAEA adopts an attention-based architecture to capture optimization states from both true and surrogate evaluation spaces, while ensuring scalability across problem dimensions, population sizes, and objectives. Additionally, we integrate TabPFN as the surrogate model for accurate and efficient prediction with uncertainty estimation. The framework is trained via reinforcement learning, leveraging parallel sampling and centralized training to enhance efficiency and transferability across tasks. Experimental results demonstrate that DB-SAEA not only outperforms state-of-the-art baselines across diverse benchmarks, but also exhibits strong zero-shot transfer to unseen tasks with higher-dimensional settings. This work introduces the first MetaBBO framework with dual-level control over SAEAs and a bi-space ELA that captures surrogate model information.

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AAAI Conference 2026 Conference Paper

P-SLCR: Unsupervised Point Cloud Semantic Segmentation via Prototypes Structure Learning and Consistent Reasoning

Lixin Zhan
Jiang Jie
Tianjian Zhou
Yukun Du
Yan Zheng
Xuehu Duan

Current semantic segmentation approaches for point cloud scenes heavily rely on manual labeling, while research on unsupervised semantic segmentation methods specifically for raw point clouds is still in its early stages. Unsupervised point cloud learning poses significant challenges due to the absence of annotation information and the lack of pre-training. The development of effective strategies is crucial in this context. In this paper, we propose a novel prototype library-driven unsupervised point cloud semantic segmentation strategy that utilizes Structure Learning and Consistent Reasoning (P-SLCR). First, we propose a Consistent Structure Learning to establish structural feature learning between consistent points and the library of consistent prototypes by selecting high-quality features. Second, we propose a Semantic Relation Consistent Reasoning that constructs a prototype inter-relation matrix between consistent and ambiguous prototype libraries separately. This process ensures the preservation of semantic consistency by imposing constraints on consistent and ambiguous prototype libraries through the prototype inter-relation matrix. Finally, our method was extensively evaluated on the S3DIS, SemanticKITTI, and Scannet datasets, achieving the best performance compared to unsupervised methods. Specifically, the mIoU of 47.1% is achieved for Area-5 of the S3DIS dataset, surpassing the classical fully supervised method PointNet by 2.5%.

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EAAI Journal 2024 Journal Article

A high-efficiency local and global detector for diatom-based drowning diagnosis

Jiehang Deng
Jianfa Yang
Haomin Wei
Guosheng Gu
Qingqing Xiang
Yukun Du
Chao Liu
Zhiqiang Chen

Detection and classification of diatoms has been considered as the most effective method for the drowning diagnosis in forensic practice. However, challenges (such as limited samples, complex background interference, and detecting multiple objects) remain during detection and classification. To address these challenges, we propose a MDMS (multi-scale dynamic multi-head self-attention)-based deep learning network for the diatom-based drowning diagnosis. This framework uses TFFT (two-stage full fine-tuning training strategy) to solve the challenges of limited diatom samples and detecting multiple objects, and builds a multi-scale dynamic multi-head self-attention module to extract the local and global features of diatom images. In the meantime, we introduce an online hard example mining strategy to attenuate the complex background interference. The experimental results show that the proposed framework can effectively reduce the missing and false detection rates of diatom objects, with the mAP (mean Average Precision) reaching 92. 434%, which is better than the result using the mainstream methods.

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JBHI Journal 2022 Journal Article

Attention Gate Based Dual-Pathway Network for Vertebra Segmentation of X-Ray Spine Images

Wenbo Shi
Tongshuai Xu
Huan Yang
Yongming Xi
Yukun Du
Jinhua Li
Jinxu Li

Automatic spine and vertebra segmentation from X-ray spine images is a critical and challenging problem in many computer-aid spinal image analysis and disease diagnosis applications. In this paper, a two-stage automatic segmentation framework for spine X-ray images is proposed, which can firstly locate the spine regions (including backbone, sacrum and ilium) in the coarse stage and then identify eighteen vertebrae (i. e. , cervical vertebra 7, thoracic vertebra 1-12 and lumbar vertebra 1-5) with isolate and clear boundary in the fine stage. A novel Attention Gate based dual-pathway Network (AGNet) composed of context and edge pathways is designed to extract semantic and boundary information for segmentation of both spine and vertebra regions. Multi-scale supervision mechanism is applied to explore comprehensive features and an Edge aware Fusion Mechanism (EFM) is proposed to fuse features extracted from the two pathways. Some other image processing skills, such as centralized backbone clipping, patch cropping and convex hull detection are introduced to further refine the vertebra segmentation results. Experimental validations on spine X-ray images dataset and vertebrae dataset suggest that the proposed AGNet achieves superior performance compared with state-of-the-art segmentation methods, and the coarse-to-fine framework can be implemented in real spinal diagnosis systems.

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