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Haijiang Li

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5 papers

1 author row

AAAI Conference 2026 Conference Paper

Primary Visual Cortex Inspired Point Cloud Analysis Framework

Jisheng Dang
Delin Deng
Bimei Wang
Jingze Wu
Hui Zhang
Haijiang Li
Jingmei Jiao
Dengyue Pan

Despite significant advancements in point cloud analysis, reducing energy consumption and improving robustness remain understudied, largely due to the inherent limitations of Convolutional Neural Networks (CNNs). To address this, we take the cue from the primary visual cortex and propose a Dendritic-Connected Continuous-Coupled Neural Network (DC-CCNN), a novel Brain-Inspired Neural Network (BINN) architecture tailored for point cloud analysis. By leveraging the unique characteristics of point clouds, our design combines discrete and continuous encoding, replacing traditional Multilayer Perceptrons (MLPs) with more efficient and robust BINNs. Our approach substantially improves the performance of Brain-Inspired Neural Networks on point analysis tasks and maintaining performance comparable to state-of-the-art methods. Furthermore, DC-CCNN exhibits enhanced robustness against various point cloud deformations and corruptions. Our experimental results demonstrate that DC-CCNN achieves competitive performance on benchmark datasets, making it a promising alternative to traditional deep learning methods for point cloud analysis. With its high efficiency and robustness, DC-CCNN has the potential for widespread adoption in 3D computer vision, robotics, and autonomous systems.

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AAAI Conference 2026 Conference Paper

Realistic Curriculum Reinforcement Learning for Autonomous and Sustainable Marine Vessel Navigation

Xiaocai Zhang
Zhe Xiao
Maohan Liang
Tao Liu
Haijiang Li
Wenbin Zhang

Sustainability is becoming increasingly critical in the maritime transport, encompassing both environmental and social impacts, such as Greenhouse Gas (GHG) emissions and navigational safety. Traditional vessel navigation heavily relies on human experience, often lacking autonomy and emission awareness, and is prone to human errors that may compromise safety. In this paper, we propose a Curriculum Reinforcement Learning (CRL) framework integrated with a realistic, data-driven marine simulation environment and a machine learning-based fuel consumption prediction module. The simulation environment is constructed using real-world vessel movement data and enhanced with a Diffusion Model to simulate dynamic maritime conditions. Vessel fuel consumption is estimated using historical operational data and learning-based regression. The surrounding environment is represented as image-based inputs to capture spatial complexity. We design a lightweight, policy-based CRL agent with a comprehensive reward mechanism that considers safety, emissions, timeliness, and goal completion. This framework effectively handles complex tasks progressively while ensuring stable and efficient learning in continuous action spaces. We validate the proposed approach in a sea area of the Indian Ocean, demonstrating its efficacy in enabling sustainable and safe vessel navigation.

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EAAI Journal 2023 Journal Article

Few-shot learning for image-based bridge damage detection

Yan Gao
Haijiang Li
Weiqi Fu

Autonomous bridge visual inspection is a real-world challenge due to various materials, surface coatings, and changing light and weather conditions. Traditional supervised learning relies on massive annotated data to establish a robust model, which requires a time-consuming data acquisition process. This work proposes a few-shot learning (FSL) approach based on improved ProtoNet for damage detection with just a few labeled examples. Feature embedding is achieved through cross-domain transfer learning from ImageNet instead of episodic training. The ProtoNet is improved with embedding normalization to enhance transduction performance based on Euclidean distance and a linear classifier for classification. The approach is explored on a public dataset through different ablation experiments and achieves over 94% mean accuracy for 2-way 5-shot classification via the pre-trained GoogleNet after fine-tuning. Moreover, the proposed fine-tuning methods based on a fully connected layer (FCN) and Hadamard product are demonstrated with better performance than the previous method. Finally, the approach is validated using real bridge inspection images, demonstrating its capability of fast implementation for practical damage inspection with weakly supervised information.

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YNIMG Journal 2014 Journal Article

Association of creative achievement with cognitive flexibility by a combined voxel-based morphometry and resting-state functional connectivity study

Qunlin Chen
Wenjing Yang
Wenfu Li
Dongtao Wei
Haijiang Li
Qiao Lei
Qinglin Zhang
Jiang Qiu

Although researchers generally concur that creativity involves the production of novel and useful products, the neural basis of creativity remains elusive due to the complexity of the cognitive processes involved. Recent studies have shown that highly creative individuals displayed more cognitive flexibility. However, direct evidence supporting the relationship between creativity and cognitive flexibility has rarely been investigated using both structural and functional neuroimaging techniques. We used a combined voxel-based morphometry and resting-state functional connectivity (rsFC) analysis to investigate the relationship between individual creativity ability assessed by the creative achievement questionnaire (CAQ), and regional gray matter volume (GMV), as well as intrinsic functional connectivity. Results showed that CAQ scores negatively correlated with GMV in the rostral anterior cingulate cortex (ACC) and the bilateral dorsal ACC (dACC) extending to supplementary motor area, but positively correlated with GMV in the bilateral superior frontal gyrus and ventral medial prefrontal cortex (vmPFC). Further functional connectivity analysis revealed that higher creative achievement was inversely associated with the strength of rsFC between the dACC and medial superior frontal gyrus (mSFG), right middle frontal gyrus, and left orbito-frontal insula. Moreover, the association between the dACC–mSFG connectivity and CAQ scores was mediated by cognitive flexibility, assessed by a task-switching paradigm. These findings indicate that individual differences in creative achievement are associated with both brain structure and corresponding intrinsic functional connectivity involved in cognitive flexibility and deliberate creative processing. Furthermore, dACC–mSFG connectivity may affect creative achievement through its impact on cognitive flexibility.

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YNIMG Journal 2014 Journal Article

Examining brain structures associated with perceived stress in a large sample of young adults via voxel-based morphometry

Haijiang Li
Wenfu Li
Dongtao Wei
Qunlin Chen
Todd Jackson
Qinglin Zhang
Jiang Qiu

Perceived stress reflects the extent to which situations are appraised as stressful at a given point in one's life. Past brain imaging studies have examined activation patterns underlying the stress response, yet focal differences in brain structures related to perceived stress are not well understood, especially when considering gray matter (GM) and white matter (WM) structures simultaneously. In this study, voxel-based morphometry was used to investigate relations between GM/WM volume and perceived stress levels in a large young adult sample. Participants (138 men, 166 women) completed the Perceived Stress Scale (PSS; Cohen et al. , 1983) and underwent an anatomical magnetic resonance imaging scan. Higher PSS scores were associated with larger GM volume in a cluster that included regions in the bilateral parahippocampal gyrus, fusiform cortex, and entorhinal cortex and smaller GM volume in a cluster that included regions of the right insular cortex. Higher PSS scores were also related to smaller WM volume in a cluster that included the body of the corpus callosum. This pattern of results remained significant even after controlling for effects of general intelligence, socioeconomic status, and depression. Together, findings suggest a unique structural basis for individual differences in perceived stress, distributed across different GM and WM regions of the brain.

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