Author name cluster

Jun Wei

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

TG-Field: Geometry-Aware Radiative Gaussian Fields for Tomographic Reconstruction

Yuxiang Zhong
Jun Wei
Chaoqi Chen
Senyou An
Hui Huang

3D Gaussian Splatting (3DGS) has revolutionized 3D scene representation with superior efficiency and quality. While recent adaptations for computed tomography (CT) show promise, they struggle with severe artifacts under highly sparse-view projections and dynamic motions. To address these challenges, we propose Tomographic Geometry Field (TG-Field), a geometry-aware Gaussian deformation framework tailored for both static and dynamic CT reconstruction. A multi-resolution hash encoder is employed to capture local spatial priors, regularizing primitive parameters under ultra-sparse settings. We further extend the framework to dynamic reconstruction by introducing time-conditioned representations and a spatiotemporal attention block to adaptively aggregate features, thereby resolving spatiotemporal ambiguities and enforcing temporal coherence. In addition, a motion-flow network models fine-grained respiratory motion to track local anatomical deformations. Extensive experiments on synthetic and real-world datasets demonstrate that TG-Field consistently outperforms existing methods, achieving state-of-the-art reconstruction accuracy under highly sparse-view conditions.

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AAAI Conference 2025 Conference Paper

An LLM-Empowered Adaptive Evolutionary Algorithm for Multi-Component Deep Learning Systems

Haoxiang Tian
Xingshuo Han
Guoquan Wu
An Guo
Yuan Zhou
Jie Zhang
Shuo Li
Jun Wei

Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep learning (MCDL) systems face challenges in enhancing the search efficiency while maintaining the diversity. To combat these, this paper proposes the first LLM-empowered adaptive evolutionary search algorithm to detect safety violations in MCDL systems. Inspired by the context-understanding ability of Large Language Models (LLMs), our approach promotes the LLM to comprehend the optimization problem and generate an initial population tailed to evolutionary objectives. Subsequently, it employs adaptive selection and variation to iteratively produce offspring, balancing the evolutionary efficiency and diversity. During the evolutionary process, to navigate away from the local optima, our approach integrates the evolutionary experience back into the LLM. This utilization harnesses the LLM's quantitative reasoning prowess to generate differential seeds, breaking away from current optimal solutions. We evaluate our approach in finding safety violations of MCDL systems, and compare its performance with state-of-the-art MOEA methods. Experimental results show that our approach can significantly improve the efficiency and diversity of the evolutionary search.

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IROS Conference 2025 Conference Paper

Dual-Mode Passive Fault-Tolerant Control for Underwater Vehicles with Actuator Faults and Time-Varying Disturbances

Yizong Chen
Jun Wei
Zhiqiang Miao
Kangcheng Liu
Yaonan Wang 0001

This paper investigates the control problem of underwater vehicles subject to time-varying external disturbances and actuator faults. A novel passive fault-tolerant control (PFTC) scheme is developed to address the coupled disturbance-fault dynamics inherent in underwater vehicle systems. The proposed dual-mode architecture comprises: 1) a robust fault-tolerant control scheme based on high-order sliding mode observers (HOSMOs) for minor fault scenarios, which effectively compensates for bounded disturbances and partial actuator degradation; 2) a conditionally triggered estimation mechanism integrated with fault-tolerant control allocation (FTCA) and HOSMOs for severe fault conditions, enabling fault estimation and model compensation via event-triggered parameter updating. The hybrid architecture ensures computational efficiency by activating the estimation module only when predefined triggering conditions are violated. Comprehensive experimental results validate the superiority of the proposed method in maintaining stability and performance under various fault conditions. This work provides a systematic solution for underwater vehicle control under coupled disturbance-fault conditions, with verified real-time performance and implementation feasibility.

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AAAI Conference 2024 Conference Paper

WeakPCSOD: Overcoming the Bias of Box Annotations for Weakly Supervised Point Cloud Salient Object Detection

Jun Wei
S. Kevin Zhou
Shuguang Cui
Zhen Li

Point cloud salient object detection (PCSOD) is a newly proposed task in 3D dense segmentation. However, the acquisition of accurate 3D dense annotations comes at a high cost, severely limiting the progress of PCSOD. To address this issue, we propose the first weakly supervised PCSOD (named WeakPCSOD) model, which relies solely on cheap 3D bounding box annotations. In WeakPCSOD, we extract noise-free supervision from coarse 3D bounding boxes while mitigating shape biases inherent in box annotations. To achieve this, we introduce a novel mask-to-box (M2B) transformation and a color consistency (CC) loss. The M2B transformation, from a shape perspective, disentangles predictions from labels, enabling the extraction of noiseless supervision from labels while preserving object shapes independently of the box bias. From an appearance perspective, we further introduce the CC loss to provide dense supervision, which mitigates the non-unique predictions stemming from weak supervision and substantially reduces prediction variability. Furthermore, we employ a self-training (ST) strategy to enhance performance by utilizing high-confidence pseudo labels. Notably, the M2B transformation, CC loss, and ST strategy are seamlessly integrated into any model and incur no computational costs for inference. Extensive experiments demonstrate the effectiveness of our WeakPCSOD model, even comparable to fully supervised models utilizing dense annotations.

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IJCAI Conference 2021 Conference Paper

Adaptive Residue-wise Profile Fusion for Low Homologous Protein Secondary Structure Prediction Using External Knowledge

Qin Wang
Jun Wei
Boyuan Wang
Zhen Li
Sheng Wang
Shuguang Cui

Protein secondary structure prediction (PSSP) is essential for protein function analysis. However, for low homologous proteins, the PSSP suffers from insufficient input features. In this paper, we explicitly import external self-supervised knowledge for low homologous PSSP under the guidance of residue-wise (amino acid wise) profile fusion. In practice, we firstly demonstrate the superiority of profile over Position-Specific Scoring Matrix (PSSM) for low homologous PSSP. Based on this observation, we introduce the novel self-supervised BERT features as the pseudo profile, which implicitly involves the residue distribution in all native discovered sequences as the complementary features. Furthermore, a novel residue-wise attention is specially designed to adaptively fuse different features (i. e. , original low-quality profile, BERT based pseudo profile), which not only takes full advantage of each feature but also avoids noise disturbance. Besides, the feature consistency loss is proposed to accelerate the model learning from multiple semantic levels. Extensive experiments confirm that our method outperforms state-of-the-arts (i. e. , 4. 7% for extremely low homologous cases on BC40 dataset).

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AAAI Conference 2020 Conference Paper

F³Net: Fusion, Feedback and Focus for Salient Object Detection

Jun Wei
Shuhui Wang
Qingming Huang

Most of existing salient object detection models have achieved great progress by aggregating multi-level features extracted from convolutional neural networks. However, because of the different receptive ﬁelds of different convolutional layers, there exists big differences between features generated by these layers. Common feature fusion strategies (addition or concatenation) ignore these differences and may cause suboptimal solutions. In this paper, we propose the F3 Net to solve above problem, which mainly consists of cross feature module (CFM) and cascaded feedback decoder (CFD) trained by minimizing a new pixel position aware loss (PPA). Speciﬁcally, CFM aims to selectively aggregate multilevel features. Different from addition and concatenation, CFM adaptively selects complementary components from input features before fusion, which can effectively avoid introducing too much redundant information that may destroy the original features. Besides, CFD adopts a multi-stage feedback mechanism, where features closed to supervision will be introduced to the output of previous layers to supplement them and eliminate the differences between features. These reﬁned features will go through multiple similar iterations before generating the ﬁnal saliency maps. Furthermore, different from binary cross entropy, the proposed PPA loss doesn’t treat pixels equally, which can synthesize the local structure information of a pixel to guide the network to focus more on local details. Hard pixels from boundaries or error-prone parts will be given more attention to emphasize their importance. F3 Net is able to segment salient object regions accurately and provide clear local details. Comprehensive experiments on ﬁve benchmark datasets demonstrate that F3 Net outperforms state-of-the-art approaches on six evaluation metrics. Code will be released at https: //github. com/weijun88/F3Net.

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