Author name cluster

Bingjun Luo

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.

5 papers

1 author row

EAAI Journal 2025 Journal Article

Fully cooperative domain adaptive neural network for defect classification in printed circuit boards

Jiafu Wen
Yuebin Wu
Wenkai Huang
Kunbo Han
Bingjun Luo

The rapid development of deep learning algorithms has enabled effective identification of real and pseudo defects in Printed Circuit Boards (PCBs) when sufficient annotated data are available. However, many types of PCBs typically contain unique defect types and circuit specifications and often lack sufficient training datasets for defect detection. Using them to train models with high-precision classification capabilities remains a challenge. In response to these challenges, this study proposes Fully Cooperative Domain Adaptive Neural Network (FC-DANN), aimed at utilizing similar principles for defect recognition on circuit boards of different styles. The FC-DANN model integrates a cyclic feature extractor with a transformation unit and a collaborative discriminator with non-adversarial features. At the same time, a co-quotient classifier composed of multiple single-domain label classifiers is introduced. This method can effectively achieve feature alignment of data with different styles for high-precision, small sample target domain classification. This study use datasets for four PCB styles as well as the public dataset, the efficacy of the FC-DANN model was confirmed. The proposed method has been proven to be effective, with average classification accuracy exceeding 1. 06% and 1. 88% of existing technologies on both datasets, respectively.

Details DOI

AAAI Conference 2025 Conference Paper

Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval

Bingjun Luo
Jinpeng Wang
Zewen Wang
Junjie Zhu
Xibin Zhao

Video surveillance systems are crucial components for ensuring public safety and management in smart city. As a fundamental task in video surveillance, text-to-image person retrieval aims to retrieve the target person from an image gallery that best matches the given text description. Most existing text-to-image person retrieval methods are trained in a supervised manner that requires sufficient labeled data in the target domain. However, it is common in practice that only unlabeled data is available in the target domain due to the difficulty and cost of data annotation, which limits the generalization of existing methods in practical application scenarios. To address this issue, we propose a novel unsupervised domain adaptation method, termed Graph-Based Cross-Domain Knowledge Distillation (GCKD), to learn the cross-modal feature representation for text-to-image person retrieval in a cross-dataset scenario. The proposed GCKD method consists of two main components. Firstly, a graph-based multi-modal propagation module is designed to bridge the cross-domain correlation among the visual and textual samples. Secondly, a contrastive momentum knowledge distillation module is proposed to learn the cross-modal feature representation using the online knowledge distillation strategy. By jointly optimizing the two modules, the proposed method is able to achieve efficient performance for cross-dataset text-to-image person retrieval. Extensive experiments on three publicly available text-to-image person retrieval datasets demonstrate the effectiveness of the proposed GCKD method, which consistently outperforms the state-of-the-art baselines.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition

Bingjun Luo
Haowen Wang
Jinpeng Wang
Junjie Zhu
Xibin Zhao
Yue Gao

With the strong robusticity on illumination variations, near-infrared (NIR) can be an effective and essential complement to visible (VIS) facial expression recognition in low lighting or complete darkness conditions. However, facial expression recognition (FER) from NIR images presents a more challenging problem than traditional FER due to the limitations imposed by the data scale and the difficulty of extracting discriminative features from incomplete visible lighting contents. In this paper, we give the first attempt at deep NIR facial expression recognition and propose a novel method called near-infrared facial expression transformer (NFER-Former). Specifically, to make full use of the abundant label information in the field of VIS, we introduce a Self-Attention Orthogonal Decomposition mechanism that disentangles the expression information and spectrum information from the input image, so that the expression features can be extracted without the interference of spectrum variation. We also propose a Hypergraph-Guided Feature Embedding method that models some key facial behaviors and learns the structure of the complex correlations between them, thereby alleviating the interference of inter-class similarity. Additionally, we construct a large NIR-VIS Facial Expression dataset that includes 360 subjects to better validate the efficiency of NFER-Former. Extensive experiments and ablation studies show that NFER-Former significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition

Bingjun Luo
Zewen Wang
Jinpeng Wang
Junjie Zhu
Xibin Zhao
Yue Gao

Illumination variation has been a long-term challenge in real-world facial expression recognition (FER). Under uncontrolled or non-visible light conditions, near-infrared (NIR) can provide a simple and alternative solution to obtain high-quality images and supplement the geometric and texture details that are missing in the visible (VIS) domain. Due to the lack of large-scale NIR facial expression datasets, directly extending VIS FER methods to the NIR spectrum may be ineffective. Additionally, previous heterogeneous image synthesis methods are restricted by low controllability without prior task knowledge. To tackle these issues, we present the first approach, called for NIR-FER Stochastic Differential Equations (NFER-SDE), that transforms face expression appearance between heterogeneous modalities to the overfitting problem on small-scale NIR data. NFER-SDE can take the whole VIS source image as input and, together with domain-specific knowledge, guide the preservation of modality-invariant information in the high-frequency content of the image. Extensive experiments and ablation studies show that NFER-SDE significantly improves the performance of NIR FER and achieves state-of-the-art results on the only two available NIR FER datasets, Oulu-CASIA and Large-HFE.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition

Bingjun Luo
Junjie Zhu
Tianyu Yang
Sicheng Zhao
Chao Hu
Xibin Zhao
Yue Gao

Existing methods on facial expression recognition (FER) are mainly trained in the setting when multi-class data is available. However, to detect the alien expressions that are absent during training, this type of methods cannot work. To address this problem, we develop a Hierarchical Spatial One Class Facial Expression Recognition Network (HS-OCFER) which can construct the decision boundary of a given expression class (called normal class) by training on only one-class data. Specifically, HS-OCFER consists of three novel components. First, hierarchical bottleneck modules are proposed to enrich the representation power of the model and extract detailed feature hierarchy from different levels. Second, multi-scale spatial regularization with facial geometric information is employed to guide the feature extraction towards emotional facial representations and prevent the model from overfitting extraneous disturbing factors. Third, compact intra-class variation is adopted to separate the normal class from alien classes in the decision space. Extensive evaluations on 4 typical FER datasets from both laboratory and wild scenarios show that our method consistently outperforms state-of-the-art One-Class Classification (OCC) approaches.

PDF Details DOI