Few-shot defect multi-classification (FSDMC) is an emerging trend in quality control within industrial manufacturing. However, current FSDMC research often lacks generalizability due to its focus on specific datasets. Additionally, defect classification heavily relies on contextual information within images, and existing methods fall short of effectively extracting this information. To address these challenges, we propose a general FSDMC framework called MVREC, which offers two primary advantages: (1) MVREC extracts general features for defect instances by incorporating the pre-trained AlphaCLIP model. (2) It utilizes a region-context framework to enhance defect features by leveraging mask region input and multi-view context augmentation. Furthermore, Few-shot Zip-Adapter(-F) classifiers within the model are introduced to cache the visual features of the support set and perform few-shot classification. We also introduce MVTec-FS, a new FSDMC benchmark based on MVTec AD, which includes 1228 defect images with instance-level mask annotations and 46 defect types. Extensive experiments conducted on MVTec-FS and four additional datasets demonstrate its effectiveness in general defect classification and its ability to incorporate contextual information to improve classification performance.

Recently, many methods based on low-rank representation have been proposed for fabric defect detection. Most of them relax the low-rank decomposition problem to a nuclear norm minimization (NNM) problem to pursue the convexity of the objective function. When solving the standard NNM problem, matrix singular values have to be treated equally. This, however, would be impractical in the scenario of fabric defect detection as the matrix singular values have clear physical meanings, and thus, they should be treated differently. In this article, we propose a weighted double-low-rank decomposition method (WDLRD) to treat the matrix singular values differently by assigning different weights. Thus, the most important/distinguishing characteristics of a fabric image can be preserved. Another difference between WDLRD and the other existing low-rank-based methods is that WDLRD considers a defective fabric image being decomposed to two low-rank matrices, i.e., low-rank defect-free matrix and low-rank defect matrix, as the defect-free and defective regions are usually composed of homogeneous objects that have a high correlation. Besides, WDLRD is more robust for defect detection in various situations by adding a noise term to avoid noise or other interference on the fabric surface. In addition, a defect prior is incorporated into the objective function of WDLRD to guide locating the defective regions. The proposed optimization problem can be easily solved by an iterative algorithm based on augmented Lagrange multipliers. Experimental results on TILDA, periodically patterned fabric, and Textile & Apparel Artificial Intelligence databases show that the proposed WDLRD obtains better performance than state-of-the-art methods in locating the defective regions on fabric images.Note to Practitioners—This article is motivated by the problem that the performance of fabric defect detection in the textile industry is poor. It is necessary to develop an effective method to improve the defect detection accuracy and reduce overall manufacturing cost. Existing automatic defect detection approaches usually contain two stages: first, capture fabric images from the weaving machine and then use a defect detection algorithm in a host computer to conduct a real-time inspection and give an alarm if defects occur. This article focuses on locating defects for given defective images after the procedure of binary classification (which determines an image as defective or defect free). The article proposes an objective function to mathematically interpret the optimization problem between fabric images and predictive defects. The optimal solution can be obtained by employing the alternating direction method of multipliers (ADMMs). The proposed method is described as a new defect detection algorithm. Extensive experiments were conducted to evaluate the algorithm, and the experimental results indicate that the proposed method is superior to many existing fabric defect detection methods. Preliminary experiments suggest that this method is feasible but has not yet been really used in production. In future research, we will collect more fabric images from the textile industry and develop large-scale databases for verifying the proposed method for real-life applications.

As the extended version of conventional Ridge Regression, L2,1-norm based ridge regression learning methods have been widely used in subspace learning since they are more robust than Frobenius norm based regression and meanwhile guarantee joint sparsity. However, conventional L2,1-norm regression methods encounter the small-class problem and meanwhile ignore the local geometric structures, which degrade their performances. To address these problems, we propose a novel regression method called Locality Preserving Robust Regression (LPRR). In addition to using the L2,1-norm for jointly sparse regression, we also utilize capped L2-norm in loss function to further enhance the robustness of the proposed algorithm. Moreover, to make use of local structure information, we also integrate the property of locality preservation into our model since it is of great importance in dimensionality reduction. The convergence analysis and computational complexity of the proposed iterative algorithm are presented. Experimental results on four datasets indicate that the proposed LPRR performs better than some famous subspace learning methods in classification tasks.

This paper proposes a novel method called Jointly Sparse Locality Regression (JSLR) for feature extraction and selection. JSLR utilizes joint L2,1-norm minimization on regularization term, and also introduces the locality to characterize the local geometric structure of the data. There are three main contributions in JSLR for face recognition. Firstly, it eliminates the drawback in ridge regression and Linear Discriminant Analysis (LDA) that when the number of the classes is too small, not enough projections can be obtained for feature extraction. Secondly, by using the local geometric structure as the regularization term, JSLR is able to preserve local information and find an embedding subspace which can detect the most essential data manifold structure. Moreover, since the L2,1-norm based loss function is robust to outliers in data points, JSLR provides the joint sparsity for robust feature selection. The theoretical connections of the proposed method and the previous regression methods are explored and the convergence of the proposed algorithm is also proved. Experimental evaluation on several well-known data sets shows the merits of the proposed method on feature selection and classification.

Ridge regression is widely used in multiple variable data analysis. However, in very high-dimensional cases such as image feature extraction and recognition, conventional ridge regression or its extensions have the small-class problem, that is, the number of the projections obtained by ridge regression is limited by the number of the classes. In this paper, we proposed a novel method called generalized robust regression (GRR) for jointly sparse subspace learning which can address the problem. GRR not only imposes L2,1-norm penalty on both loss function and regularization term to guarantee the joint sparsity and the robustness to outliers for effective feature selection, but also utilizes L2,1-norm as the measurement to take the intrinsic local geometric structure of the data into consideration to improve the performance. Moreover, by incorporating the elastic factor on the loss function, GRR can enhance the robustness to obtain more projections for feature selection or classification. To obtain the optimal solution of GRR, an iterative algorithm was proposed and the convergence was also proved. Experiments on six wellknown data sets demonstrate the merits of the proposed method. The result indicates that GRR is a robust and efficient regression method for face recognition.

Classical linear discriminant analysis (LDA) has the limitation that it requires the within-class scatter matrix to be nonsingular so that it can perform eigen-decomposition to obtain optimal solutions. To break through this limitation, many methods based on LDA have been proposed. However, these methods are either sensitive to outliers or lack joint sparsity for effective feature extraction. To release these problems, this paper proposes a locally joint sparse marginal embedding (LJSME) method. LJSME reconstructs the scatter matrices and utilizes the locality graph to weigh each pair of data, such that it is robust to outliers and able to preserve the neighborhood relationship of the data. Moreover, LJSME can easily avoid the small sample-size problem by a maximum margin criterion and obtain joint sparsity for effective feature extraction by using joint sparse regularization. The comprehensive analysis between the proposed LJSME and the related methods is presented, which indicates the advantages of the proposed method. A series of experiments was conducted to evaluate the performance of LJSME when compared with the state-of-the-art methods. The MATLAB code of LJSME can be downloaded fromhttps://github.com/TungmeeMo/LJSME.git.

Ridge regression (RR) and its extended versions are widely used as an effective feature extraction method in pattern recognition. However, the RR-based methods are sensitive to the variations of data and can learn only limited number of projections for feature extraction and recognition. To address these problems, we propose a new method called robust discriminant regression (RDR) for feature extraction. In order to enhance the robustness, the L2,1-norm is used as the basic metric in the proposed RDR. The designed robust objective function in regression form can be solved by an iterative algorithm containing an eigenfunction, through which the optimal orthogonal projections of RDR can be obtained by eigen decomposition. The convergence analysis and computational complexity are presented. In addition, we also explore the intrinsic connections and differences between the RDR and some previous methods. Experiments on some well-known databases show that RDR is superior to the classical and very recent proposed methods reported in the literature, no matter the L2-norm or the L2,1-norm-based regression methods. The code of this paper can be downloaded from http://www.scholat.com/laizhihui.