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Xin Qiu

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3 papers

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EAAI Journal 2026 Journal Article

A lightweight framework with adaptive feature enhancement for accurate pavement distress evaluation

Yi Liang
Jueqiang Tao
Qing Yang
Xin Qiu
Tingfeng Zhang
Yafang Liu
Heng Zhou

Timely pavement condition survey ensures optimal pavement performance and extends its service life. However, existing lightweight object detection models for pavement distress identification often struggle with a trade-off between computational efficiency and fine-grained feature extraction, fail to adapt to the irregular, elongated morphologies of cracks using fixed-grid convolutions, and are hindered by class imbalance and complex backgrounds that lead to misclassifications. To address these gaps, this study proposes the Lightweight Pavement Distress Network (LPD-Net), a crack-feature enhanced framework based on You Only Look Once version 11 (YOLOv11) for accurate pavement distress detection. Firstly, a large-scale dataset comprising depth images was constructed using a three-dimensional (3D) laser imaging sensor. Secondly, Dynamic Snake Convolution (DySConv) was integrated into the Cross Stage Partial with kernel size 2 (C3k2) module to adaptively adjust kernel sampling for better capturing crack contours and edges. Thirdly, a Bi-level Routing Attention (BRA) module was embedded to dynamically filter background noise and focus on sparse distress features, alleviating class imbalance. Lastly, a Lightweight Asymmetric Detection Head (LADH) incorporating Depthwise Separable Convolution (DSConv) was designed to reduce computational overhead while maintaining localization precision. Experimental results demonstrate that LPD-Net achieves a superior balance, reducing computational cost by 15. 9 % to 5. 3 Giga Floating Point Operations (GFLOPs) compared to the baseline while increasing mean Average Precision at 50 % intersection over union (mAP@50) by 6. 5 % to 0. 506. Measurement-oriented evaluation via Pavement Condition Index (PCI) further confirms its reliability, with 40. 72 % agreement within ± 5 PCI, aligning well with metrological standards.

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NeurIPS Conference 2024 Conference Paper

Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space

Xin Qiu
Risto Miikkulainen

With the widespread application of Large Language Models (LLMs) to various domains, concerns regarding the trustworthiness of LLMs in safety-critical scenarios have been raised, due to their unpredictable tendency to hallucinate and generate misinformation. Existing LLMs do not have an inherent functionality to provide the users with an uncertainty/confidence metric for each response it generates, making it difficult to evaluate trustworthiness. Although several studies aim to develop uncertainty quantification methods for LLMs, they have fundamental limitations, such as being restricted to classification tasks, requiring additional training and data, considering only lexical instead of semantic information, and being prompt-wise but not response-wise. A new framework is proposed in this paper to address these issues. Semantic density extracts uncertainty/confidence information for each response from a probability distribution perspective in semantic space. It has no restriction on task types and is "off-the-shelf" for new models and tasks. Experiments on seven state-of-the-art LLMs, including the latest Llama 3 and Mixtral-8x22B models, on four free-form question-answering benchmarks demonstrate the superior performance and robustness of semantic density compared to prior approaches.

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AAAI Conference 2022 Conference Paper

Detecting Misclassification Errors in Neural Networks with a Gaussian Process Model

Xin Qiu
Risto Miikkulainen

As neural network classifiers are deployed in real-world applications, it is crucial that their failures can be detected reliably. One practical solution is to assign confidence scores to each prediction, then use these scores to filter out possible misclassifications. However, existing confidence metrics are not yet sufficiently reliable for this role. This paper presents a new framework that produces a quantitative metric for detecting misclassification errors. This framework, RED, builds an error detector on top of the base classifier and estimates uncertainty of the detection scores using Gaussian Processes. Experimental comparisons with other error detection methods on 125 UCI datasets demonstrate that this approach is effective. Further implementations on two probabilistic base classifiers and two large deep learning architecture in vision tasks further confirm that the method is robust and scalable. Third, an empirical analysis of RED with out-of-distribution and adversarial samples shows that the method can be used not only to detect errors but also to understand where they come from. RED can thereby be used to improve trustworthiness of neural network classifiers more broadly in the future.

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