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Biao Wang

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12 papers

2 author rows

EAAI Journal 2026 Journal Article

Enhancing surface defect detection in industrial products through few-shot learning via meta-learning

Quanyou Zhang
Yong Feng
Yanying Chen
Biao Wang
Yaohui Li
Baohua Qiang
Zhangli Lan

Industrial surface-defect inspection still relies heavily on large-scale annotated datasets. Three key challenges persist: (i) extracting stable descriptors from high-dimensional, sparsely populated, and highly skewed feature spaces; (ii) avoiding label collapse when penalty biases enter into gradient-based meta-learning; and (iii) sustaining recall once annotations become scarce. To address these challenges, we propose a lightweight few-shot learning based on meta-learning (FSL-Meta) framework. First, anisotropic structures are highlighted using a Hessian eigen-operator; second, we incorporate You Only Look Once (YOLO) weights pre-trained on the Common Objects in Context dataset to enrich the low-shot feature pool; finally, a dual-task gradient update embeds signed perturbations into a fractional-order penalty, ensuring synchronous optimization of primary and auxiliary losses. Experiments on the few-shot dataset (nine defect classes of auto door and window parts) demonstrate that FSL-Meta trained with only ten support examples achieves 97. 5% mean Average Precision (mAP)@0. 5 on a single Tesla P40. This outperforms YOLOv11-n, YOLOv10-n, YOLOv9-c, YOLOv8-n and the reference Few-shot Object Detection (FSOD) method by 7. 7, 9. 3, 11. 9, and 20. 7 percentage points in mAP@0. 5, respectively. Ablation experiments further confirm that the four components are distinct yet complementary, as the removal of any single module results in a noticeable deterioration of performance. This study provides novel insights for enhancing the applicability and adaptability of few-shot learning models in practical industrial scenarios.

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EAAI Journal 2025 Journal Article

Synthetic data enhancement using diffusion models for improved miscanthus identification and bioenergy extraction

Xinyue Wang
Xiangdong Chen
Jun Jiang
Ronggao Gong
Biao Wang

Miscanthus, a high-yielding perennial grass pivotal for bioenergy production, requires precise species identification to optimize bioenergy extraction. However, limited annotated spectral datasets hinder robust classification model development. To address this challenge, the paper proposes a novel diffusion probabilistic model tailored for near-infrared spectral synthesis. Unlike conventional generative approaches, the proposed model integrates a bidirectional gated recurrent unit-based temporal encoder and one dimension convolutional neural networks within a diffusion framework, augmented by a spectral attention module to prioritize critical absorption bands. This architecture uniquely addresses the sequential dependencies and subtle biochemical variations inherent in near-infrared spectral, enabling high-fidelity generation of diverse synthetic data. The diffusion process is optimized through a hybrid loss function combining variational lower bound training with mean squared error for pixel-level fidelity and maximum mean discrepancy for distributional alignment. Evaluated on 517 near-infrared spectral samples across three Miscanthus species, the proposed model outperforms traditional variational autoencoders, generative adversarial networks, and standard diffusion models in terms of sample authenticity and diversity. Incorporating synthetic data enhanced the accuracy, precision, and recall of downstream classifiers by 10%–15%, with the convolutional neural networks attaining 87% accuracy using hybrid real-synthetic training data. Remarkably, even with 50% synthetic data substitution, classification accuracy remained robust at 75%, demonstrating the model’s efficacy in mitigating data scarcity and advancing precision agriculture for bioenergy optimization.

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ICRA Conference 2024 Conference Paper

Joint Response and Background Learning for UAV Visual Tracking

Biao Wang
Wenling Li
Bin Zhang 0023
Yang Liu 0096

Correlation filter (CF)-based approaches have gained widespread attention in the field of unmanned aerial vehicle (UAV) visual tracking due to their light-weight characteristics. However, CFs are prone to generating low-quality response in challenging UAV scenarios, e. g. , fast motion and background clutter. In this paper, in order to model the tracker more robustly, we first conduct an effective regularization analysis from the perspectives of response- and background-learning. Specifically, to address response degradation, we propose a module for learning temporal consistency and reversibility of response, supplemented by a novel background-aware module to enhance the ability to learn from negative samples. In addition, we propose a fast coarse-to-fine scale search strategy, which alleviates the challenges in estimating bounding boxes under non-uniform aspect ratios. We have developed two tracker versions, namely RBLT and DeepRBLT, based on the depth of the features. Comprehensive experiments on four UAV benchmarks and one generic benchmark have indicated the superiority of our trackers compared to other state-of-the-art trackers, with enough speed for real-time applications.

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EAAI Journal 2024 Journal Article

Self-driven continual learning for class-added motor fault diagnosis based on unseen fault detector and propensity distillation

Ao Ding
Xiaojian Yi
Yong Qin
Biao Wang

Continual learning is a high-potential technique that enables intelligent motor fault diagnosis models to extend new diagnosable fault classes without costly training from scratch. However, existing continual learning methods have the following limitations. (1) They manually detect new faults, which is labor-intensive, untimely, and more importantly, may lead to mistaken diagnosis results. (2) They adopt the traditional knowledge distillation to align the absolute responses of old and new models, which alleviates catastrophic forgetting but restricts flexible learning from incremental datasets. To overcome the above limitations, this paper proposes a novel self-driven continual learning framework for class-added motor fault diagnosis, which can spontaneously detect unseen faults and perform more flexible continual learning from incremental datasets. For the automatic detection of unseen faults, after collecting online samples, adversarial training with exemplars of each seen class is conducted to measure the class separability. The truth fault classes that are unseen for diagnosis models can be clearly distinguished from all seen classes, and correspondingly missed diagnosis or misdiagnosing can be avoided effectively and incremental samples with new fault types can be collected quickly. For the flexible continual learning strategy, a more flexible knowledge distillation is proposed to preserve the prediction propensity rather than the absolute response. This strategy not only keeps the recognition performance of old classes but also loosens unnecessary constraints and increases the diagnosis model plasticity to learn new knowledge from incremental datasets, thus improving the accuracy of motor fault diagnosis during continual learning. The effectiveness of the proposed method is verified by conducting fault simulation experiments of three-phase motors and its superiority is also demonstrated by comparing it with some state-of-the-art diagnosis methods.

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AAAI Conference 2023 Conference Paper

Learning Polysemantic Spoof Trace: A Multi-Modal Disentanglement Network for Face Anti-spoofing

Kaicheng Li
Hongyu Yang
Binghui Chen
Pengyu Li
Biao Wang
Di Huang

Along with the widespread use of face recognition systems, their vulnerability has become highlighted. While existing face anti-spoofing methods can be generalized between attack types, generic solutions are still challenging due to the diversity of spoof characteristics. Recently, the spoof trace disentanglement framework has shown great potential for coping with both seen and unseen spoof scenarios, but the performance is largely restricted by the single-modal input. This paper focuses on this issue and presents a multi-modal disentanglement model which targetedly learns polysemantic spoof traces for more accurate and robust generic attack detection. In particular, based on the adversarial learning mechanism, a two-stream disentangling network is designed to estimate spoof patterns from the RGB and depth inputs, respectively. In this case, it captures complementary spoofing clues inhering in different attacks. Furthermore, a fusion module is exploited, which recalibrates both representations at multiple stages to promote the disentanglement in each individual modality. It then performs cross-modality aggregation to deliver a more comprehensive spoof trace representation for prediction. Extensive evaluations are conducted on multiple benchmarks, demonstrating that learning polysemantic spoof traces favorably contributes to anti-spoofing with more perceptible and interpretable results.

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EAAI Journal 2023 Journal Article

Selective Feature Bagging of one-class classifiers for novelty detection in high-dimensional data

Biao Wang
Wenjing Wang
Guanglei Meng
Tiankuo Meng
Bin Song
Yingnan Wang
Yuming Guo
Zhihua Qiao

Novelty detection in high-dimensional data is a challenging task due to the masking effect of irrelevant attributes. A common solution is to discover feature subspace, of which attributes are relevant to novelties. Due to the high uncertainty of novelties in practical applications, ensemble models that combine results from multiple subspaces are proved to be more effective than single models. According to the theory of bias–variance tradeoff, existing ensembles are often developed based on variance reduction. However, it is argued that the combination of poor detectors will deteriorate the performance of ensembles. To this end, this paper proposes an ensemble detector that takes into account variance and bias reduction simultaneously. Our ensemble is referred to as Selective Feature Bagging (SFB) since it is developed on the basis of Feature Bagging (FB). In order to improve the accuracy without deterioration of diversity of base detectors in FB, we resort to the notion of dynamic classifier selection which is proved be effective in classification. During the ensemble generation phase, base detectors are produced and categorized into different groups that are distinguished by the dimensionality of subspace used for training. The purpose of such a design is to maintain the diversity. During the generation phase, the most competent base detector from each of groups is dynamically selected and used to make decision on the test pattern. The purpose of such a design is to enhance the accuracy. We verify the effectiveness of SFB on 15 data sets from KEEL repository. Experimental results have shown that SFB can statistically outperform FB. In addition, several state-of-the-art have also been outperformed by SFB.

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AAAI Conference 2023 Conference Paper

Video Object of Interest Segmentation

Siyuan Zhou
Chunru Zhan
Biao Wang
Tiezheng Ge
Yuning Jiang
Li Niu

In this work, we present a new computer vision task named video object of interest segmentation (VOIS). Given a video and a target image of interest, our objective is to simultaneously segment and track all objects in the video that are relevant to the target image. This problem combines the traditional video object segmentation task with an additional image indicating the content that users are concerned with. Since no existing dataset is perfectly suitable for this new task, we specifically construct a large-scale dataset called LiveVideos, which contains 2418 pairs of target images and live videos with instance-level annotations. In addition, we propose a transformer-based method for this task. We revisit Swin Transformer and design a dual-path structure to fuse video and image features. Then, a transformer decoder is employed to generate object proposals for segmentation and tracking from the fused features. Extensive experiments on LiveVideos dataset show the superiority of our proposed method.

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EAAI Journal 2022 Journal Article

Boosting the prediction of molten steel temperature in ladle furnace with a dynamic outlier ensemble

Biao Wang
Wenjing Wang
Guanglei Meng
Zhihua Qiao
Yuming Guo
Na Wang
Wei Wang
Zhizhong Mao

Molten steel temperature prediction is a critical step in the development of level-two control systems for ladle furnace. Many machine learning algorithms have been employed to complete such a work. Whereas data-driven predictors often deteriorate due to the presence of outliers in practical applications. This paper proposes to boost the predictive performance via outlier detection. Specifically, a dynamic outlier ensemble is developed inspired by the superiority of dynamic classifier selection in classification. Clustering analysis is used to determine the region of competence, on which base detectors are selected with the dedicated measure. The reason for the usage of clustering analysis lies in its efficiency during online detection. One attribute weighting algorithm is used to enhance the capability of clustering in outlier detection. The information behind regression is used to facilitate the measure of competence, results of which can promote the performance of predictors. Such a strategy can achieve double-win from the perspective of regression and outlier detection. Extensive experiments on real-world data sets show that results of all 4 predictive models with respect to accuracy and hit rate can be improved. Moreover, the detection performance in terms of G-mean and F1 score of our detector has also been confirmed via the comparison with 8 competitors.

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EAAI Journal 2022 Journal Article

Dynamic selective Gaussian process regression for forecasting temperature of molten steel in ladle furnace

Biao Wang
Wenjing Wang
Zhihua Qiao
Guanglei Meng
Zhizhong Mao

The requirement for intelligent steelmaking has underlined the significance of data-driven predictions of molten steel temperature in ladle furnace. Recently, predictors based on ensemble learning have shown their superiority over single ones. However, the strong reliability on the ensemble diversity can hardly insure their generalization ability. Moreover, most existing predictors cannot provide statistical meaning to their outputs. This has degraded their engineering value. In this paper, we aim to address these two problems in one scheme, where a dynamic regression ensemble of Gaussian process models is built. Our dynamic ensemble will select the most competent individual for each test pattern according to the competence estimated by informative neighbors. To this end, a distance measure based on RReliefF is constructed to search for these neighbors, rather than traditional K-nearest neighbor. Several evaluation indexes are combined by a meta regressor so that more robust estimation of competence can be achieved. A Bayesian nonparametric model is used for ensemble generation in order to obtain statistical predictions. A data set from real-world ladle furnace is used to verify the effectiveness of the proposed predictor. According to the comparative results, we have found the superiority of our dynamic ensemble over static ensembles and single predictors. Furthermore, the improvement over existing dynamic ensembles has also been confirmed.

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AAAI Conference 2021 Conference Paper

Adversarial Pose Regression Network for Pose-Invariant Face Recognitions

Pengyu Li
Biao Wang
Lei Zhang

Face recognition has achieved significant progress in recent years. However, the large pose variation between face images remains a challenge in face recognition. We observe that the pose variation in the hidden feature maps is one of the most critical factors to hinder the representations from being pose-invariant. Based on the observation, we propose an Adversarial Pose Regression Network (APRN) to extract poseinvariant identity representations by disentangling their pose variation in hidden feature maps. To model the pose discriminator in APRN as a regression task in its 3D space, we also propose an Adversarial Regression Loss Function and extend the adversarial learning from classification problems to regression problems in this paper. Our APRN is a plug-andplay structure that can be embedded in other state-of-the-art face recognition algorithms to improve their performance additionally. The experiments show that the proposed APRN consistently and significantly boosts the performance of baseline networks without extra computational costs in the inference phase. APRN achieves comparable or even superior to the state-of-the-art on CFP, Multi-PIE, IJB-A and MegaFace datasets. The code will be released1, hoping to nourish our proposals to other computer vision fields.

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AAAI Conference 2021 Conference Paper

Deep Metric Learning with Graph Consistency

Binghui Chen
Pengyu Li
Zhaoyi Yan
Biao Wang
Lei Zhang

Deep Metric Learning (DML) has been more attractive and widely applied in many computer vision tasks, in which a discriminative embedding is requested such that the image features belonging to the same class are gathered together and the ones belonging to different classes are pushed apart. Most existing works insist to learn this discriminative embedding by either devising powerful pair-based loss functions or hardsample mining strategies. However, in this paper, we start from another perspective and propose Deep Consistent Graph Metric Learning (CGML) framework to enhance the discrimination of the learned embedding. It is mainly achieved by rethinking the conventional distance constraints as a graph regularization and then introducing a Graph Consistency regularization term, which intends to optimize the feature distribution from a global graph perspective. Inspired by the characteristic of our defined ’Discriminative Graph’, which regards DML from another novel perspective, the Graph Consistency regularization term encourages the sub-graphs randomly sampled from the training set to be consistent. We show that our CGML indeed serves as an efficient technique for learning towards discriminative embedding and is applicable to various popular metric objectives, e. g. Triplet, N-Pair and Binomial losses. This paper empirically and experimentally demonstrates the effectiveness of our graph regularization idea, achieving competitive results on the popular CUB, CARS, Stanford Online Products and In-Shop datasets.

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AAAI Conference 2016 Conference Paper

DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks

Biao Wang
Ge Chen
Luoyi Fu
Li Song
Xinbing Wang
Xue Liu

Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor inﬂuence minimization with user experience (DRIMUX). Our goal is to minimize the inﬂuence of the rumor (i. e. , the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on realistic scenario. In addition, different from existing problems of in- ﬂuence minimization, we take into account the constraint of user experience utility. Speciﬁcally, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.

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