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Tao Lu

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8 papers

1 author row

EAAI Journal 2026 Journal Article

Power System state prediction method based on improved long short-term memory considering renewable energy uncertainty

Yue Yu
Chihan Zhou
Yue Wang
Tao Lu
Ziqi Fan

To address the multi-dimensional uncertainty issues brought by high-penetration renewable energy sources grid integration, a power system state prediction method based on improved long short-term memory (LSTM) considering renewable energy uncertainty is proposed. First, a deep bidirectional long short-term memory neural network (Deep Bi-LSTM) is employed as the foundation of the Bayesian framework, capturing uncertainties in active distribution networks through posterior inference. Second, to address the limitations of the improved Deep Bi-LSTM model in feature extraction, an attention mechanism is introduced and a global-attention long short-term memory (GLSTM) model is constructed to strengthen the correlation between input features and target features. Finally, a novel state prediction method is proposed, which utilizes the GLSTM model to predict dynamic state variables, achieving overall modeling and quantification of both model uncertainty and aleatory uncertainty. Through simulation experiments conducted on IEEE 69-bus standard test systems, it is verified that the proposed Bayesian deep learning (BDL) method can effectively capture both types of uncertainties and achieve high-precision state prediction, providing a new feasible approach for the field of state prediction.

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AAAI Conference 2025 Conference Paper

Matching While Perceiving: Enhance Image Feature Matching with Applicable Semantic Amalgamation

Shihua Zhang
Zhenjie Zhu
Zizhuo Li
Tao Lu
Jiayi Ma

Image feature matching is a cardinal problem in computer vision, aiming to establish accurate correspondences between two-view images. Existing methods are constrained by the performance of feature extractors and struggle to capture local information affected by sparse texture or occlusions. Recognizing that human eyes consider not only similar local geometric features but also high-level semantic information of scene objects when matching images, this paper introduces SemaGlue. This novel algorithm perceives and incorporates semantic information into the matching process. In contrast to recent approaches that leverage semantic consistency to narrow the scope of matching areas, SemaGlue achieves semantic amalgamation with the designed Semantic-Aware Fusion (SAF) Block by injecting abundant semantic features from the pre-trained segmentation model. Moreover, the Cross-Domain Alignment (CDA) Block is proposed to address domain alignment issues, bridging the gaps between semantic and geometric domains to ensure applicable semantic amalgamation. Extensive experiments demonstrate that SemaGlue outperforms state-of-the-art methods across various applications such as homography estimation, relative pose estimation, and visual localization.

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YNIMG Journal 2025 Journal Article

The association among individual gray matter volume of frontal-limbic circuitry, fatigue susceptibility, and comorbid neuropsychiatric symptoms following COVID-19

Xuan Niu
Wenrui Bao
Zhaoyao Luo
Pang Du
Heping Zhou
Haiyang Liu
Baoqi Wang
Huawen Zhang

BACKGROUND: Fatigue is often accompanied by comorbid sleep disturbance and psychiatric distress following the COVID-19 infection. However, identifying individuals at risk for developing post-COVID fatigue remains challenging. This study aimed to identify the neurobiological markers underlying fatigue susceptibility and further investigate their effect on COVID-19-related neuropsychiatric symptoms. METHODS: Individuals following a mild SARS-CoV-2 infection (COV+) underwent neuropsychiatric measurements (n = 335) and MRI scans (n = 271) within 1 month (baseline), and 191 (70.5 %) of the individuals were followed up 3 months after infection. Sixty-seven healthy controls (COV-) completed the same recruitment protocol. RESULTS: Whole-brain voxel-wise analysis showed that gray matter volume (GMV) during the acute phase did not differ between the COV+ and COV- groups. GMV in the right dorsolateral prefrontal cortex (DLPFC) and left dorsal anterior cingulate cortex (dACC) were associated with fatigue severity only in the COV+ group at baseline, which were assigned to the frontal system and limbic system, respectively. Furthermore, fatigue mediated the associations between volume differences in fatigue susceptibility and COVID-related sleep, post-traumatic stress disorder, anxiety and depression. Crucially, the initial GMV in the right DLPFC can predict fatigue symptoms 3 months after infection. CONCLUSIONS: We provide novel evidence on the neuroanatomical basis of fatigue vulnerability and emphasize that acute fatigue is an important link between early GMV in the frontal-limbic regions and comorbid neuropsychiatric symptoms at baseline and 3 months after infection. Our findings highlight the role of the frontal-limbic system in predisposing individuals to develop post-COVID fatigue.

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AAAI Conference 2024 Conference Paper

A Robust Mutual-Reinforcing Framework for 3D Multi-Modal Medical Image Fusion Based on Visual-Semantic Consistency

Hao Zhang
Xuhui Zuo
Huabing Zhou
Tao Lu
Jiayi Ma

This work proposes a robust 3D medical image fusion framework to establish a mutual-reinforcing mechanism between visual fusion and lesion segmentation, achieving their double improvement. Specifically, we explore the consistency between vision and semantics by sharing feature fusion modules. Through the coupled optimization of the visual fusion loss and the lesion segmentation loss, visual-related and semantic-related features will be pulled into the same domain, effectively promoting accuracy improvement in a mutual-reinforcing manner. Further, we establish the robustness guarantees by constructing a two-level refinement constraint in the process of feature extraction and reconstruction. Benefiting from full consideration for common degradations in medical images, our framework can not only provide clear visual fusion results for doctor's observation, but also enhance the defense ability of lesion segmentation against these negatives. Extensive evaluations of visual fusion and lesion segmentation scenarios demonstrate the advantages of our method in terms of accuracy and robustness. Moreover, our proposed framework is generic, which can be well-compatible with existing lesion segmentation algorithms and improve their performance. The code is publicly available at https://github.com/HaoZhang1018/RMR-Fusion.

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NeurIPS Conference 2024 Conference Paper

GSDF: 3DGS Meets SDF for Improved Neural Rendering and Reconstruction

Mulin Yu
Tao Lu
Linning Xu
Lihan Jiang
Yuanbo Xiangli
Bo Dai

Representing 3D scenes from multiview images remains a core challenge in computer vision and graphics, requiring both reliable rendering and reconstruction, which often conflicts due to the mismatched prioritization of image quality over precise underlying scene geometry. Although both neural implicit surfaces and explicit Gaussian primitives have advanced with neural rendering techniques, current methods impose strict constraints on density fields or primitive shapes, which enhances the affinity for geometric reconstruction at the sacrifice of rendering quality. To address this dilemma, we introduce GSDF, a dual-branch architecture combining 3D Gaussian Splatting (3DGS) and neural Signed Distance Fields (SDF). Our approach leverages mutual guidance and joint supervision during the training process to mutually enhance reconstruction and rendering. Specifically, our method guides the Gaussian primitives to locate near potential surfaces and accelerates the SDF convergence. This implicit mutual guidance ensures robustness and accuracy in both synthetic and real-world scenarios. Experimental results demonstrate that our method boosts the SDF optimization process to reconstruct more detailed geometry, while reducing floaters and blurry edge artifacts in rendering by aligning Gaussian primitives with the underlying geometry.

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EAAI Journal 2024 Journal Article

Small object detection by Edge-aware Neural Network

Xianhong Zhang
Tao Lu
Jiaming Wang
Shichang Fu
Fangqun Gao

The object detection method is widely applied in industrial inspections. However, many detectors face challenges in accurately capturing the blurred edge details of small objects, resulting in inaccurate bounding box predictions. To address this, we propose an Edge-aware Neural Network (EANN) for small object detection. Firstly, we introduce a Channel and Spatial Attention Fusion Module (CSAFM) to enhance the edge features of small objects, enabling the network to extract more discriminative information. Next, we propose a Multiple Aggregation Feature Pyramid (MAFP) to integrate multi-scale deep features into shallow features. This fusion enriches the shallow features with abundant semantic information, thereby aiding in the detection of small objects. Additionally, we propose a Side and Center Point Aligned Intersection over Union loss (SCPAIoULoss) to enhance the bounding box regression when there is minimal overlap between predicted and ground truth boxes. SCPAIoULoss combines Side Ratio (SR) loss, Center Point Distance (CPD) loss, and Intersection over Union (IoU) loss. The utilization of SR Loss directly constrains the width and height regression of bounding boxes, while CPD loss introduces stricter constraints to facilitate bounding box regression. Furthermore, IoU loss promotes the overall regression of predicted boxes. We extensively experiment on Tiny CityPersons, WiderFace, and our proposed dataset of base station data centers to validate the effectiveness of our method. The results indicate that our method surpasses several State-of-The-Art (SOTA) approaches in small object detection and can be effectively applied to the task of inspecting base station data centers.

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AAAI Conference 2022 Conference Paper

Degrade Is Upgrade: Learning Degradation for Low-Light Image Enhancement

Kui Jiang
Zhongyuan Wang
Zheng Wang
Chen Chen
Peng Yi
Tao Lu
Chia-Wen Lin

Low-light image enhancement aims to improve an image’s visibility while keeping its visual naturalness. Different from existing methods tending to accomplish the relighting task directly by ignoring the fidelity and naturalness recovery, we investigate the intrinsic degradation and relight the lowlight image while refining the details and color in two steps. Inspired by the color image formulation (diffuse illumination color plus environment illumination color), we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refine the content to recover the loss of diffuse illumination color. To this end, we propose a novel Degradation-to-Refinement Generation Network (DRGN). Its distinctive features can be summarized as 1) A novel two-step generation network for degradation learning and content refinement. It is not only superior to one-step methods, but also capable of synthesizing sufficient paired samples to benefit the model training; 2) A multi-resolution fusion network to represent the target information (degradation or contents) in a multi-scale cooperative manner, which is more effective to address the complex unmixing problems. Extensive experiments on both the enhancement task and joint detection task have verified the effectiveness and efficiency of our proposed method, surpassing the SOTA by 0. 70dB on average and 3. 18% in mAP, respectively. The code will be available soon.

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AAAI Conference 2019 Conference Paper

Discrete Social Recommendation

Chenghao Liu
Xin Wang
Tao Lu
Wenwu Zhu
Jianling Sun
Steven Hoi

Social recommendation, which aims at improving the performance of traditional recommender systems by considering social information, has attracted broad range of interests. As one of the most widely used methods, matrix factorization typically uses continuous vectors to represent user/item latent features. However, the large volume of user/item latent features results in expensive storage and computation cost, particularly on terminal user devices where the computation resource to operate model is very limited. Thus when taking extra social information into account, precisely extracting K most relevant items for a given user from massive candidates tends to consume even more time and memory, which imposes formidable challenges for efficient and accurate recommendations. A promising way is to simply binarize the latent features (obtained in the training phase) and then compute the relevance score through Hamming distance. However, such a two-stage hashing based learning procedure is not capable of preserving the original data geometry in the real-value space and may result in a severe quantization loss. To address these issues, this work proposes a novel discrete social recommendation (DSR) method which learns binary codes in a unified framework for users and items, considering social information. We further put the balanced and uncorrelated constraints on the objective to ensure the learned binary codes can be informative yet compact, and finally develop an efficient optimization algorithm to estimate the model parameters. Extensive experiments on three real-world datasets demonstrate that DSR runs nearly 5 times faster and consumes only with 1/37 of its real-value competitor’s memory usage at the cost of almost no loss in accuracy.

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