Author name cluster

Yufei Yang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

EAAI Journal 2025 Journal Article

A multimodal industrial anomaly detection method based on mask training and teacher–student joint memory

Yi Liu
Changsheng Zhang
Xingjun Dong
Yufei Yang

In recent years, the teacher–student framework has been applied to both single-modality detection and multimodal detection, which realizes anomaly detection based on the feature difference between the teacher model and the student model. However, current multimodal teacher–student models use the same teacher model to extract two-dimensional (2D) image and three-dimensional (3D) point cloud features. The point cloud features extracted by the teacher model pre-trained on images are not the optimal feature representation. To further improve the performance of the teacher–student framework on the multimodal anomaly detection task, this paper proposes Multimodal Teacher-Student Joint Memory (MTSJM). MTSJM constructs a teacher–student joint memory bank for each modality, the feature distance between the test sample and the memory bank is used as the anomaly indicator. This distance reflects the feature differences between the test sample and the normal sample at multiple levels, including the teacher–teacher, teacher–student, and student–student levels. Then, this paper proposes a mask-based student model training method. While ensuring that the student learns the feature of normal regions, mask training increases the feature difference of non-normal regions between the student and the teacher. On the MVTec 3D Anomaly Detection (MVTec 3D-AD) dataset, the proposed MTSJM achieves effective anomaly detection performance, reaching 95. 7% mean Image-level Area Under the Receiver Operator Curve (I-AUROC) and 97. 2% mean Area Under the Per-Region Overlap (AUPRO). In addition, MTSJM achieves 99. 3% I-AUROC and 99. 6% Pixel-level AUROC (P-AUROC) on a real-world vehicle stamping part task, which further illustrates the applicability of MTSJM on the multimodal anomaly detection task.

Details DOI

JBHI Journal 2025 Journal Article

DA-META: A Dual Attention Meta-Learning Framework for Unsupervised Motor Imagery Decoding

Jianhang Liu
Mingai Li
Zhi Li
Yufei Yang
Qi Qi

Motor imagery electroencephalography (MI-EEG) decoding demonstrates significant potential for paralysis rehabilitation, and its generalization capability is often compromised by intersubject variability and scarcity of labeled target domain data. Meta-learning has emerged as a promising approach for unsupervised domain adaptation problem. However, existing implementations suffer from two critical limitations: insufficient feature extraction and overlooking the guiding role of unlabeled target data. To overcome these challenges, we propose a dual-attention meta-learning framework (DA-META) with model-agnostic architecture in this paper. The framework comprises three stages: meta-task construction, guided meta-training, and fine-tuning-free meta-testing. In the guided meta-training stage, DA-META incorporates two key attention mechanisms: an enhanced temporal attention module for effective feature extraction, and a cosine similarity-based attention module to leverage the guidance of target domain. Using EEGNet as the backbone network, DA-META achieves mean classification accuracies of 68. 04% and 76. 61% on self-collected datasets from patients and healthy subjects, and 73. 29% and 80. 93% on the public BCI Competition IV 2a and 2b datasets, outperforming state-of-the-art methods. When employing EEGNet, DeepConvNet, and EEG Conformer as backbone networks respectively, the framework achieves accuracy improvements of 5. 17%, 2. 56%, and 0. 85% on the 2a dataset, compared to the baseline. These results demonstrate the framework's superior ability to handle inter-subject variability and its significant potential to improve practical applicability.

Details DOI

IJCAI Conference 2025 Conference Paper

Do You Steal My Model? Signature Diffusion Embedded Dual-Verification Watermarking for Protecting Intellectual Property of Hyperspectral Image Classification Models

Yufei Yang
Song Xiao
Lixiang Li
Wenqian Dong
Jiahui Qu

Due to the high cost of data collection and training, the well-performed hyperspectral image (HSI) classification models are of great value and vulnerable to piracy threat during transmission and use. Model watermarking is a promising technology for intellectual property (IP) protection of models. However, the existing model watermarking methods for RGB image classification models ignore the complexity of ground objects and high dimension of HSIs, which makes trigger samples easy to be detected and forged. To address this problem, we propose a signature diffusion embedded dual-verification watermarking method, which generates imperceptible trigger samples with explicit owner information to achieve dual verification of both model ownership and legality of trigger set. Specifically, the subpixel-space owner signature diffusion incorporated imperceptible trigger set generation method is proposed to manipulate owner signature incorporated to the abundance matrix of seeds via diffusion model in subpixel space, thus balancing the perceptual quality of trigger samples and signature extraction capability. To resist ownership confusion, dual-stamp ownership verification is proposed to query the suspicious model with trigger samples for ownership verification, and further extracts signature from trigger samples to guarantee their legality. Extensive experiments demonstrate the proposed method can effectively protect IP of HSI classification models.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Template3D-AD: Point Cloud Template Matching Method Based on Center Points for 3D Anomaly Detection

Yi Liu
Changsheng Zhang
Yufei Yang

Existing 3D anomaly detection methods mainly include reconstruction-based methods and memory-based methods. However, reconstruction-based methods rely on anomaly simulation strategies, while the memory bank of memory-based methods cannot cover the features of all points. Different from existing methods, this paper proposes Template3D-AD, a 3D anomaly detection method based on template matching. Template3D-AD matches the test sample with the template based on center points, and extracts the global features and local features of the center point respectively. Considering that the appearance of anomalies is related to the change of surface shape, this paper proposes a curvature-based local feature representation method, which increases the feature difference between abnormal surfaces and normal surfaces. Then, this paper designs a global-local detection strategy, which combines global feature differences and local feature differences for anomaly detection. Extensive experiments show that Template3D-AD outperforms the state-of-the-art methods, achieving 84. 4% (1. 5% ↑) I-AUROC on the Real3D-AD dataset and 86. 5% (11. 6% ↑) I-AUROC on the Anomaly-ShapeNet dataset. Code at https: //github. com/CaedmonLY/Template3D-AD.

PDF Details DOI

AAAI Conference 2024 Conference Paper

LDS2AE: Local Diffusion Shared-Specific Autoencoder for Multimodal Remote Sensing Image Classification with Arbitrary Missing Modalities

Jiahui Qu
Yuanbo Yang
Wenqian Dong
Yufei Yang

Recent research on the joint classification of multimodal remote sensing data has achieved great success. However, due to the limitations imposed by imaging conditions, the case of missing modalities often occurs in practice. Most previous researchers regard the classification in case of different missing modalities as independent tasks. They train a specific classification model for each fixed missing modality by extracting multimodal joint representation, which cannot handle the classification of arbitrary (including multiple and random) missing modalities. In this work, we propose a local diffusion shared-specific autoencoder (LDS2AE), which solves the classification of arbitrary missing modalities with a single model. The LDS2AE captures the data distribution of different modalities to learn multimodal shared feature for classification by designing a novel local diffusion autoencoder which consists of a modality-shared encoder and several modality-specific decoders. The modality-shared encoder is designed to extract multimodal shared feature by employing the same parameters to map multimodal data into a shared subspace. The modality-specific decoders put the multimodal shared feature to reconstruct the image of each modality, which facilitates the shared feature to learn unique information of different modalities. In addition, we incorporate masked training to the diffusion autoencoder to achieve local diffusion, which significantly reduces the training cost of model. The approach is tested on widely-used multimodal remote sensing datasets, demonstrating the effectiveness of the proposed LDS2AE in addressing the classification of arbitrary missing modalities. The code is available at https://github.com/Jiahuiqu/LDS2AE.

PDF Details DOI

AAAI Conference 2021 Conference Paper

FL-MSRE: A Few-Shot Learning based Approach to Multimodal Social Relation Extraction

Hai Wan
Manrong Zhang
Jianfeng Du
Ziling Huang
Yufei Yang
Jeff Z. Pan

Social relation extraction (SRE for short), which aims to infer the social relation between two people in daily life, has been demonstrated to be of great value in reality. Existing methods for SRE consider extracting social relation only from unimodal information such as text or image, ignoring the high coupling of multimodal information. Moreover, previous studies overlook the serious unbalance distribution on social relations. To address these issues, this paper proposes FL-MSRE, a few-shot learning based approach to extracting social relations from both texts and face images. Considering the lack of multimodal social relation datasets, this paper also presents three multimodal datasets annotated from four classical masterpieces and corresponding TV series. Inspired by the success of BERT, we propose a strong BERT based baseline to extract social relation from text only. FL-MSRE is empirically shown to outperform the baseline significantly. This demonstrates that using face images benefits text-based SRE. Further experiments also show that using two faces from different images achieves similar performance as from the same image. This means that FL-MSRE is suitable for a wide range of SRE applications where the faces of two people can only be collected from different images. 1

PDF Details

AAAI Conference 2020 Conference Paper

Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis

Hai Wan
Yufei Yang
Jianfeng Du
Yanan Liu
Kunxun Qi
Jeff Z. Pan

Aspect-based sentiment analysis (ABSA) aims to detect the targets (which are composed by continuous words), aspects and sentiment polarities in text. Published datasets from SemEval-2015 and SemEval-2016 reveal that a sentiment polarity depends on both the target and the aspect. However, most of the existing methods consider predicting sentiment polarities from either targets or aspects but not from both, thus they easily make wrong predictions on sentiment polarities. In particular, where the target is implicit, i. e. , it does not appear in the given text, the methods predicting sentiment polarities from targets do not work. To tackle these limitations in ABSA, this paper proposes a novel method for target-aspectsentiment joint detection. It relies on a pre-trained language model and can capture the dependence on both targets and aspects for sentiment prediction. Experimental results on the SemEval-2015 and SemEval-2016 restaurant datasets show that the proposed method achieves a high performance in detecting target-aspect-sentiment triples even for the implicit target cases; moreover, it even outperforms the state-of-theart methods for those subtasks of target-aspect-sentiment detection that they are competent to.

PDF Details