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Linbo Xie

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4 papers

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EAAI Journal 2026 Journal Article

A robust recognition algorithm for unknown event rejection in distributed fiber optic sensing

Zijie Lin
Fei Cheng
Linbo Xie

Distributed Fiber Optic Sensing (DFOS) systems are widely deployed in monitoring scenarios, where reliable recognition of intrusion events is essential. However, most existing deep learning models operate under a closed-set assumption and lack the capability to reject unseen disturbances in open-world environments, leading to severe performance degradation when unknown events occur. To address this challenge, we propose a Time-Frequency Rejective Autoencoder (TF-RAE) for robust known-event recognition and unknown-event rejection. The proposed framework integrates a Local–Global Frequency Integrator (LGFI) with multiscale temporal convolution to capture complementary time–frequency representations. Furthermore, a Similarity-Constrained Reconstruction (SCR) loss is introduced to enhance structural discrimination by enforcing similarity consistency at both sample and batch levels, overcoming the limitations of conventional L1/L2 reconstruction losses. Experimental results on a real-world DOFS dataset demonstrate that TF-RAE achieves 95. 3% classification accuracy on known events while attaining 100% unknown event rejection accuracy. These results verify the effectiveness and robustness of the proposed approach for open-set recognition in distributed sensing systems.

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EAAI Journal 2025 Journal Article

Adaptive temporal fusion network with depth supervision and modulation for robust three-dimensional object detection in complex scenes

Yifan Liu
Yong Zhang
Rukai Lan
Xiaopeng Cui
Linbo Xie
Zhaolong Wu

Autonomous driving perception relies on cameras and Light Detection and Ranging (LiDAR) sensors. Existing methods for LiDAR-camera fusion are primarily based on the Lift-Splat (LS) framework, which serves as a foundation for multi-modal fusion. However, these methods still face challenges such as unreliable depth information, insufficient dynamic perception, and limited robustness. This paper proposes a novel multi-modal three-dimensional (3D) detection method that optimizes depth by fully leveraging image and point cloud data and employs spatiotemporal deformable attention for adaptive fusion across frames. Specifically, we generate optimized depth maps through point clouds for depth supervision, refine the depth using Conditional Random Fields (CRF), and improve the fusion features by optimizing the depth estimation range. Additionally, we propose a dual-alignment method with spatiotemporal adaptive attention to acquire high-quality temporal features, allowing the model to learn beneficial information from adjacent frames. The proposed method achieves leading mean Average Precision (mAP) on mainstream 3D object detection datasets. Extensive experiments on multiple datasets demonstrate the superiority of the proposed method. Notably, our method remains effective even when a sensor fails, highlighting its potential to improve the robustness of autonomous perception in real-world scenarios.

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AAAI Conference 2025 Conference Paper

Parametric ρ-Norm Scaling Calibration

Siyuan Zhang
Linbo Xie

Output uncertainty indicates whether the probabilistic properties of the overall distribution reflect objective characteristics of the model output. Unlike most loss functions and metrics in machine learning, uncertainty pertains to individual samples, but validating it on individual samples is unfeasible. When validated collectively, it cannot fully represent individual sample properties, posing a challenge in assessing and calibrating model confidence in a limited data set. Hence, it is crucial to consider confidence calibration characteristics. To counter the adverse effects of the gradual amplification of the classifier output amplitude in supervised learning, we introduce a post-processing parametric calibration method, ρ-Norm Scaling, which expands the calibrator expression and mitigates overconfidence due to excessive amplitude while preserving accuracy. Moreover, calibrator optimization based bin-level calibration error often results in the loss of significant instance-level information. Therefore, we include probability distribution regularization, which incorporates a priori information that the instance-level uncertainty distribution after calibration should resemble the distribution before calibration. Experimental results demonstrate the substantial enhancement in the post-processing calibrator for uncertainty calibration with our proposed method.

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NeurIPS Conference 2024 Conference Paper

A PID Controller Approach for Adaptive Probability-dependent Gradient Decay in Model Calibration

Siyuan Zhang
Linbo Xie

Modern deep learning models often exhibit overconfident predictions, inadequately capturing uncertainty. During model optimization, the expected calibration error tends to overfit earlier than classification accuracy, indicating distinct optimization objectives for classification error and calibration error. To ensure consistent optimization of both model accuracy and model calibration, we propose a novel method incorporating a probability-dependent gradient decay coefficient into loss function. This coefficient exhibits a strong correlation with the overall confidence level. To maintain model calibration during optimization, we utilize a proportional-integral-derivative (PID) controller to dynamically adjust this gradient decay rate, where the adjustment relies on the proposed relative calibration error feedback in each epoch, thereby preventing the model from exhibiting over-confidence or under-confidence. Within the PID control system framework, the proposed relative calibration error serves as the control system output, providing an indication of the overall confidence level, while the gradient decay rate functions as the controlled variable. Moreover, recognizing the impact of gradient amplitude of adaptive decay rates, we implement an adaptive learning rate mechanism for gradient compensation to prevent inadequate learning of over-small or over-large gradient. Empirical experiments validate the efficacy of our PID-based adaptive gradient decay rate approach, ensuring consistent optimization of model calibration and model accuracy.

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