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Xiaodong Yue

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9 papers

1 author row

AAAI Conference 2026 Conference Paper

Not All Inconsistency Is Equal: Decomposing LVLM Uncertainty into Belief Divergence and Belief Conflict

Jie Shi
Xiaodong Yue
Wei Liu
Yufei Chen
Feifan Dong

Uncertainty Quantification (UQ) is critical for detecting hallucinations in black-box Large Vision-Language Models (LVLMs). However, prevailing methods like Discrete Semantic Entropy (DSE) are unreliable, as their scores are primarily dominated by the number of semantic clusters. This renders them incapable of distinguishing between benign semantic ambiguity (varied but coherent responses) and severe belief conflict (contradictory responses). We address this limitation by proposing a novel framework rooted in Dempster-Shafer theory of evidence, built on the premise that not all inconsistency is equal. Our method decomposes uncertainty into two complementary metrics: Belief Divergence, which quantifies ambiguity by measuring the separation between viewpoints, and Belief Conflict, which captures direct logical contradictions. Extensive experiments demonstrate that our framework provides a more reliable measure of uncertainty.

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AAAI Conference 2025 Conference Paper

Enhancing Multi-View Classification Reliability with Adaptive Rejection

Wei Liu
Yufei Chen
Xiaodong Yue

Multi-view classification based on evidence theory aims to enhance result reliability by effectively quantifying prediction uncertainty at the evidence level, particularly when dealing with low-quality views. However, these methods face limitations in real-world applications due to the sensitivity of estimated uncertainty to view distribution, leading to two main issues: 1) difficulty in making clear judgments about whether to trust predictions based on vague uncertainty scores, and 2) the potential negative impact of integrating information from low-quality views on multi-view classification performance. Both limitations compromise the reliability of multi-view decisions. To address these challenges, we introduce an adaptive rejection mechanism based on estimated uncertainty, which is free of data distribution constraints. By integrating this adaptive rejection mechanism into the fusion of multiple views, our method not only indicates whether predictions should be adopted or rejected at the view level but also enhances classification performance by minimizing the impact of unreliable information. The effectiveness of our method is demonstrated through comprehensive theoretical analysis and empirical experiments on various multi-view datasets, establishing its superiority in enhancing the reliability of multi-view classification.

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NeurIPS Conference 2025 Conference Paper

Vicinal Label Supervision for Reliable Aleatoric and Epistemic Uncertainty Estimation

Linye Li
Yufei Chen
Xiaodong Yue

Uncertainty estimation is crucial for ensuring the reliability of machine learning models in safety-critical applications. Evidential Deep Learning (EDL) offers a principled framework by modeling predictive uncertainty through Dirichlet distributions over class probabilities. However, existing EDL methods predominantly rely on level-0 hard labels, which supervised a uncertainty-aware model with full certainty. We argue that hard labels not only fail to capture epistemic uncertainty but also obscure the aleatoric uncertainty arising from inherent data noise and label ambiguity. As a result, EDL models often produce degenerate Dirichlet distributions that collapse to near-deterministic outputs. To overcome these limitations, we propose a vicinal risk minimization paradigm for EDL by incorporating level-1 supervision in the form of vicinally smoothed conditional label distributions. This richer supervision exposes the model to local label uncertainty, enhancing aleatoric uncertainty quantification, while also mitigating the degeneration of the Dirichlet distribution into a Dirac delta function, thereby improving epistemic uncertainty modeling. Extensive experiments show that our approach consistently outperforms standard EDL baselines across synthetic datasets, covariate-shifted out-of-distribution generalization tasks, and out-of-distribution detection benchmarks, providing more reliable uncertainty estimates.

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NeurIPS Conference 2024 Conference Paper

Hyper-opinion Evidential Deep Learning for Out-of-Distribution Detection

Jingen Qu
Yufei Chen
Xiaodong Yue
Wei Fu
Qiguang Huang

Evidential Deep Learning (EDL), grounded in Evidence Theory and Subjective Logic (SL), provides a robust framework to estimate uncertainty for out-of-distribution (OOD) detection alongside traditional classification probabilities. However, the EDL framework is constrained by its focus on evidence that supports only single categories, neglecting the other collective evidences that could corroborate multiple in-distribution categories. This limitation leads to a diminished estimation of uncertainty and a subsequent decline in OOD detection performance. Additionally, EDL encounters the vanishing gradient problem within its fully-connected layers, further degrading classification accuracy. To address these issues, we introduce hyper-domain and propose Hyper-opinion Evidential Deep Learning (HEDL). HEDL extends the evidence modeling paradigm by explicitly integrating sharp evidence, which supports a singular category, with vague evidence that accommodates multiple potential categories. Additionally, we propose a novel opinion projection mechanism that translates hyper-opinion into multinomial-opinion, which is then optimized within the EDL framework to ensure precise classification and refined uncertainty estimation. HEDL integrates evidences across various categories to yield a holistic evidentiary foundation for achieving superior OOD detection. Furthermore, our proposed opinion projection method effectively mitigates the vanishing gradient issue, ensuring classification accuracy without additional model complexity. Extensive experiments over many datasets demonstrate our proposed method outperforms existing OOD detection methods.

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AAAI Conference 2023 Conference Paper

Safe Multi-View Deep Classification

Wei Liu
Yufei Chen
Xiaodong Yue
Changqing Zhang
Shaorong Xie

Multi-view deep classification expects to obtain better classification performance than using a single view. However, due to the uncertainty and inconsistency of data sources, adding data views does not necessarily lead to the performance improvements in multi-view classification. How to avoid worsening classification performance when adding views is crucial for multi-view deep learning but rarely studied. To tackle this limitation, in this paper, we reformulate the multi-view classification problem from the perspective of safe learning and thereby propose a Safe Multi-view Deep Classification (SMDC) method, which can guarantee that the classification performance does not deteriorate when fusing multiple views. In the SMDC method, we dynamically integrate multiple views and estimate the inherent uncertainties among multiple views with different root causes based on evidence theory. Through minimizing the uncertainties, SMDC promotes the evidences from data views for correct classification, and in the meantime excludes the incorrect evidences to produce the safe multi-view classification results. Furthermore, we theoretically prove that in the safe multi-view classification, adding data views will certainly not increase the empirical risk of classification. The experiments on various kinds of multi-view datasets validate that the proposed SMDC method can achieve precise and safe classification results.

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AAAI Conference 2023 Conference Paper

T-distributed Spherical Feature Representation for Imbalanced Classification

Xiaoyu Yang
Yufei Chen
Xiaodong Yue
Shaoxun Xu
Chao Ma

Real-world classification tasks often show an extremely imbalanced problem. The extreme imbalance will cause a strong bias that the decision boundary of the classifier is completely dominated by the categories with abundant samples, which are also called the head categories. Current methods have alleviated the imbalanced impact from mainly three aspects: class re-balance, decoupling and domain adaptation. However, the existing criterion with the winner-take-all strategy still leads to the crowding problem in the eigenspace. The head categories with many samples can extract features more accurately, but occupy most of the eigenspace. The tail categories sharing the rest of the narrow eigenspace are too crowded together to accurately extract features. Above these issues, we propose a novel T-distributed spherical metric for equalized eigenspace in the imbalanced classification, which has the following innovations: 1) We design the T-distributed spherical metric, which has the characteristics of high kurtosis. Instead of the winner-take-all strategy, the T-distributed spherical metric produces a high logit only when the extracted feature is close enough to the category center, without a strong bias against other categories. 2) The T-distributed spherical metric is integrated into the classifier, which is able to equalize the eigenspace for alleviating the crowding issue in the imbalanced problem. The equalized eigenspace by the T-distributed spherical classifier is capable of improving the accuracy of the tail categories while maintaining the accuracy of the head, which significantly promotes the intraclass compactness and interclass separability of features. Extensive experiments on large-scale imbalanced datasets verify our method, which shows superior results in the long-tailed CIFAR-100/-10 with the imbalanced ratio IR = 100/50. Our method also achieves excellent results on the large-scale ImageNet-LT dataset and the iNaturalist dataset with various backbones. In addition, we provide a case study of the real clinical classification of pancreatic tumor subtypes with 6 categories. Among them, the largest number of PDAC accounts for 315 cases, and the least CP has only 8 cases. After 4-fold cross-validation, we achieved a top-1 accuracy of 69.04%.

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AAAI Conference 2023 Conference Paper

Trusted Fine-Grained Image Classification through Hierarchical Evidence Fusion

Zhikang Xu
Xiaodong Yue
Ying Lv
Wei Liu
Zihao Li

Fine-Grained Image Classification (FGIC) aims to classify images into specific subordinate classes of a superclass. Due to insufficient training data and confusing data samples, FGIC may produce uncertain classification results that are untrusted for data applications. In fact, FGIC can be viewed as a hierarchical classification process and the multilayer information facilitates to reduce uncertainty and improve the reliability of FGIC. In this paper, we adopt the evidence theory to measure uncertainty and confidence in hierarchical classification process and propose a trusted FGIC method through fusing multilayer classification evidence. Comparing with the traditional approaches, the trusted FGIC method not only generates accurate classification results but also reduces the uncertainty of fine-grained classification. Specifically, we construct an evidence extractor at each classification layer to extract multilayer (multi-grained) evidence for image classification. To fuse the extracted multi-grained evidence from coarse to fine, we formulate evidence fusion with the Dirichlet hyper probability distribution and thereby hierarchically decompose the evidence of coarse-grained classes into fine-grained classes to enhance the classification performances. The ablation experiments validate that the hierarchical evidence fusion can improve the precision and also reduce the uncertainty of fine-grained classification. The comparison with state-of-the-art FGIC methods shows that our proposed method achieves competitive performances.

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YNIMG Journal 2022 Journal Article

Expectations of immediate and delayed reward differentially affect cognitive task performance

Yachao Rong
Ningxuan Chen
Jiarui Dong
Qi Li
Xiaodong Yue
Li Hu
Ping Wei

The current study used a modified Monetary Incentive Delay task to examine the neural mechanisms underlying anticipating and receiving an immediate or delayed reward and examined the influence of pursuing these rewards on cognitive task performance. A pre-cue indicating the potential of gaining a monetary reward (immediate-, delayed-, vs. no-reward) was followed by a target stimulus requiring a fast and accurate response. Then, response-contingent feedback was presented indicating whether or not the participant would receive the corresponding reward. Linear mixed-effect models revealed the fastest behavioural responses and the strongest neural activity, as reflected in event-related-potentials and event-related-spectral-perturbation responses, for immediate reward, followed by delayed reward, with the slowest behavioural responses and the weakest neural activities observed in the no-reward condition. Expectations related to the cue-P3 component and the cue-delta activities predicted behavioural performance, especially in the immediate reward condition. Moreover, exploratory analyses revealed that depression moderated the relationship between target-locked neural activity and behavioural performance in the delayed reward condition, with lower neural activity being related to worse behavioural performance amongst participants scoring high on depression. These results indicate that differential value representations formed through delay discounting directly affect neural responses in reward processing and directly influence the effort invested in the current task, which is reflected by behavioural responses and is in agreement with the expected value of control theory.

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AAAI Conference 2022 Conference Paper

Trusted Multi-View Deep Learning with Opinion Aggregation

Wei Liu
Xiaodong Yue
Yufei Chen
Thierry Denoeux

Multi-view deep learning is performed based on the deep fusion of data from multiple sources, i. e. data with multiple views. However, due to the property differences and inconsistency of data sources, the deep learning results based on the fusion of multi-view data may be uncertain and unreliable. It is required to reduce the uncertainty in data fusion and implement the trusted multi-view deep learning. Aiming at the problem, we revisit the multi-view learning from the perspective of opinion aggregation and thereby devise a trusted multiview deep learning method. Within this method, we adopt evidence theory to formulate the uncertainty of opinions as learning results from different data sources and measure the uncertainty of opinion aggregation as multi-view learning results through evidence accumulation. We prove that accumulating the evidences from multiple data views will decrease the uncertainty in multi-view deep learning and facilitate to achieve the trusted learning results. Experiments on various kinds of multi-view datasets verify the reliability and robustness of the proposed multi-view deep learning method.

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