Fabric defect detection is an intriguing but challenging topic. Many methods have been proposed for fabric defect detection, but these methods are still suboptimal due to the complex diversity of both fabric textures and defects. In this paper, we propose a generative adversarial network (GAN)-based framework for fabric defect detection. Considering existing challenges in real-world applications, the proposed fabric defect detection system is capable of learning existing fabric defect samples and automatically adapting to different fabric textures during different application periods. Specifically, we customize a deep semantic segmentation network for fabric defect detection that can detect different defect types. Furthermore, we attempted to train a multistage GAN to synthesize reasonable defects in new defect-free samples. First, a texture-conditioned GAN is trained to explore the conditional distribution of defects given different texture backgrounds. Given a novel fabric, we aim to generate reasonable defective patches. Then, a GAN-based fusion network fuses the generated defects to specific locations. Finally, the well-trained multistage GAN continuously updates the existing fabric defect datasets and contributes to the fine-tuning of the semantic segmentation network to better detect defects under different conditions. Comprehensive experiments on various representative fabric samples are conducted to verify the detection performance of our proposed method.

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network ( KGAN ), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e. , context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets.

Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. In detail, DPText-DETR directly leverages explicit point coordinates to generate position queries and dynamically updates them in a progressive way. Moreover, to improve the spatial inductive bias of non-local self-attention in Transformer, we present an Enhanced Factorized Self-Attention module which provides point queries within each instance with circular shape guidance. Furthermore, we design a simple yet effective positional label form to tackle the side effect of the previous form. To further evaluate the impact of different label forms on the detection robustness in real-world scenario, we establish an Inverse-Text test set containing 500 manually labeled images. Extensive experiments prove the high training efficiency, robustness, and state-of-the-art performance of our method on popular benchmarks. The code and the Inverse-Text test set are available at https://github.com/ymy-k/DPText-DETR.

Ideal SERS substrates for sensing applications should exhibit strong signal enhancement, generate a reproducible and uniform response and should be able to fabricate in large-scale and low-cost. Herein, we demonstrate low-cost, highly sensitive, disposable and reproducible SERS substrates by means of screen printing Ag nanoparticles (NPs) on a plastic PET (Polyethylene terephthalate) substrates. While there are many complex methods for the fabrication of SERS substrates, screen printing is suitable for large-area fabrication and overcomes the uneven radial distribution. Using as-printed Ag substrates as the SERS platform, detection of various commonly known chemicals have been done. The SERS detection limit of Rhodamine 6G (R6G) is higher than the concentration of 1 × 10⁻¹⁰ M. The relative standard deviation (RSD) value for 784 points on the detection of R6G and Malachite green (MG) is less than 20% revealing a homogeneous SERS distribution and high reproducibility. Moreover, melamine (MA) is detected in fresh liquid-milk without additional pretreatment, which may accelerate the application of rapid on-line detection of MA in liquid milk. Our screen printing method highlights the use of large-scale printing strategies for the fabrication of well-defined functional nanostructures with applications well beyond the field of SERS sensing.