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Baoqiang Li

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3 papers

2 author rows

ECAI Conference 2025 Conference Paper

Cross-Modal Semantic Alignment for Efficient Unsupervised Multimodal Anomaly Detection

Baoqiang Li
Tengyu Zhang
Zuo Zuo
Zongze Wu 0001

Unsupervised industrial anomaly detection aims to train a model capable of identifying diverse anomalous patterns by utilizing only normal samples. Numerous investigations have confirmed the effectiveness of such paradigms for surface anomaly detection using only 2D images. To better capture structural anomalies, recent studies have investigated Multimodal Unsupervised Industrial Anomaly Detection by jointly utilizing 2D images and 3D point cloud. Existing methods either ignore the complementarity between different modalities or consume a lot of storage space to learn and store normal features, making it difficult to balance efficient multi-modal feature utilization with computational efficiency. This paper proposes a novel and efficient unsupervised multimodal anomaly detection framework to fully exploit information from dual-modality data. By modeling latent semantic consistency of normal samples across modalities, the method detects cross-modal consistency deviations during testing for anomaly localization. Simultaneously, lightweight memory banks are separately constructed for each modality, capturing intra-modal feature inconsistencies to provide a complementary anomaly identification perspective parallel to cross-modal detection. Extensive experiments demonstrate that our framework, through simultaneously considering both inter and intra-modal consistency, achieves state-of-the-art (SOTA) detection and segmentation performance on the MVTec 3D-AD dataset with lower computational costs and faster inference speed, while maintaining robust advantages in few-shot setting.

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IJCAI Conference 2025 Conference Paper

HyperTrans: Efficient Hypergraph-Driven Cross-Domain Pattern Transfer in Image Anomaly Detection

Tengyu Zhang
Deyu Zeng
Baoqiang Li
Wei Wang
Wei Liu
Zongze Wu

Anomaly detection plays a pivotal role in industrial quality assurance processes, with cross-domain problems, exemplified by the model upgrade from RGB to 3D, being prevalent in real-world scenarios yet remaining systematically underexplored. To address the severe challenges posed by the extreme lack of datasets in target domain, we retain the knowledge from source models and explore a novel solution for anomaly detection through cross-domain learning, introducing HyperTrans. Targeting few-shot scenarios, HyperTrans centers around hypergraphs to model the relationship of the limited patch features and employs a perturbation-rectification-scoring architecture. The domain perturbation module injects and adapts channel-level statistical perturbations, mitigating style shifts during domain transfer. Subsequently, a residual hypergraph restoration module utilizes a cross-domain hypergraph to capture higher-order correlations in patches and align them across domains. Ultimately, with feature patterns exhibiting reduced domain shifts, an inter-domain scoring module aggregates similarity information between patches and normal patterns within the multi-domain subhypergraphs to make an integrated decision, generating multi-level anomaly predictions. Extensive experiments demonstrate that HyperTrans offers significant advantages in anomaly classification and anomaly segmentation tasks, outperforming state-of-the-art non-cross-domain methods in image-wise ROCAUC by 13%, 12%, and 15% in 1-shot, 2-shot, and 5-shot settings on MVTec3D AD.

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IJCAI Conference 2024 Conference Paper

CLIP-FSAC: Boosting CLIP for Few-Shot Anomaly Classification with Synthetic Anomalies

Zuo Zuo
Yao Wu
Baoqiang Li
Jiahao Dong
You Zhou
Lei Zhou
Yanyun Qu
Zongze Wu

Few-shot anomaly classification (FSAC) is a vital task in manufacturing industry. Recent methods focus on utilizing CLIP in zero/few normal shot anomaly detection instead of custom models. However, there is a lack of specific text prompts in anomaly classification and most of them ignore the modality gap between image and text. Meanwhile, there is distribution discrepancy between the pre-trained and the target data. To provide a remedy, in this paper, we propose a method to boost CLIP for few-normal-shot anomaly classification, dubbed CLIP-FSAC, which contains two-stage of training and alternating fine-tuning with two modality-specific adapters. Specifically, in the first stage, we train image adapter with text representation output from text encoder and introduce an image-to-text tuning to enhance multi-modal interaction and facilitate a better language-compatible visual representation. In the second stage, we freeze the image adapter to train the text adapter. Both of them are constrained by fusion-text contrastive loss. Comprehensive experiment results are provided for evaluating our method in few-normal-shot anomaly classification, which outperforms the state-of-the-art method by 12. 2%, 10. 9%, 10. 4% AUROC on VisA for 1, 2, and 4-shot settings.

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