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Weijie Chen

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11 papers

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JBHI Journal 2025 Journal Article

Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces

Jing Jin
Weijie Chen
Ren Xu
Wei Liang
Xiao Wu
Xinjie He
Xingyu Wang
Andrzej Cichocki

Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant aspects of realizing efficient and stable interaction, has received a great deal of intensive research. However, the current decoding methods based on deep learning are still dominated by single-scale serial convolution, which leads to insufficient extraction of abundant information from motor imagery signals. To overcome such challenges, we propose a new end-to-end convolutional neural network based on multiscale spatial-temporal feature fusion (MSTFNet) for EEG classification of motor imagery. The architecture of MSTFNet consists of four distinct modules: feature enhancement module, multiscale temporal feature extraction module, spatial feature extraction module and feature fusion module, with the latter being further divided into the depthwise separable convolution block and efficient channel attention block. Moreover, we implement a straightforward yet potent data augmentation strategy to bolster the performance of MSTFNet significantly. To validate the performance of MSTFNet, we conduct cross-session experiments and leave-one-subject-out experiments. The cross-session experiment is conducted across two public datasets and one laboratory dataset. On the public datasets of BCI Competition IV 2a and BCI Competition IV 2b, MSTFNet achieves classification accuracies of 83. 62% and 89. 26%, respectively. On the laboratory dataset, MSTFNet achieves 86. 68% classification accuracy. Besides, the leave-one-subject-out experiment is performed on the BCI Competition IV 2a dataset, and MSTFNet achieves 66. 31% classification accuracy. These experimental results outperform several state-of-the-art methodologies, indicate the proposed MSTFNet's robust capability in decoding EEG signals associated with motor imagery.

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EAAI Journal 2025 Journal Article

Understanding the impacts of negative advanced driving assistance system warnings on hazardous materials truck drivers’ responses using interpretable machine learning

Yichang Shao
Yueru Xu
Zhirui Ye
Yuhan Zhang
Weijie Chen
Nirajan Shiwakoti
XiaoMeng Shi

In recent years, Artificial Intelligence (AI) has significantly enhanced road safety, with Explainable Artificial Intelligence (XAI) providing essential transparency and trust. Our research utilizes AI to improve Advanced Driving Assistance Systems (ADAS) by investigating the gap in Forward Collision Warning (FCW): the impact of previous negative warnings (false and nuisance warnings) on drivers’ response times to subsequent accurate FCWs. By integrating XAI methods, we offer insights into the factors affecting driver behavior and system trust. Utilizing extensive dataset that encompasses various driving scenarios and driver behaviors, we constructed a gradient-boosting machine model to forecast driver response times. To explain the underlying mechanics of the model, the Shapley Additive Explanations (SHAP) framework was employed, enabling a comprehensive interpretation of feature importance and inter-feature interactions. Key findings reveal that increased speeds heighten driver responsiveness due to amplified alertness, whereas slower speeds lead to delayed reactions. The influence of previous negative warnings, significantly extends response times to accurate warnings. Additionally, older drivers require longer response times. The relationship between the driving period and previous warning judgment profoundly affects subsequent driver responsiveness, indicating trust dynamics with FCW systems. By using interpretable machine learning, we provide insights into ADAS functionality, suggesting pathways for FCW responsiveness and contributing to the field of XAI applications. In the validation experiment, our approach improved driver response times, reducing the average time from 2. 1 s to 1. 6 s. The proportion of ignored warnings decreased from 12% to 6%, and the driver acceptance rate increased from 59% to 71%.

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EAAI Journal 2024 Journal Article

Resilient automated intersection control of connected vehicles under denial of service attacks

Jian Gong
Yuan Zhao
Jinde Cao
Wei Huang
Weijie Chen
Mahmoud Abdel-Aty

This paper addresses the issue of automated intersection control for connected vehicles in the presence of Denial-of-Service (DoS) attacks. DoS attacks can disrupt the vehicular communication network, leading to increased service time and additional transmission delays, thereby raising a risk of collisions at intersections. To mitigate this problem, we propose a resilient automated intersection control system that ensures safe passage of connected vehicles through non-signalized intersections. First, we establish a communication topology that captures the information transmission between vehicles using a vehicle conflict graph. This topology serves as the basis for designing a distributed control protocol that enables conflict-free cooperation at intersections, taking into account the stochastic delays caused by DoS attacks. To synthesize a resilient controller for the automated intersection control system, we employ the polytopic overapproximation technique based on the real Jordan form. This technique allows us to model the system dynamics and uncertainties, enabling the derivation of a set of sufficient conditions for system stability. By solving a series of linear matrix inequalities, we obtain the controller gains that satisfy these stability conditions. Numerical examples are presented to demonstrate the effectiveness of the proposed control method.

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AAAI Conference 2024 Conference Paper

Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-Modal Structured Representations

Yufeng Huang
Jiji Tang
Zhuo Chen
Rongsheng Zhang
Xinfeng Zhang
Weijie Chen
Zeng Zhao
Zhou Zhao

Large-scale vision-language pre-training has achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on image-text matching tasks that require structured representations, i.e., representations of objects, attributes, and relations. The models cannot make a distinction between "An astronaut rides a horse" and "A horse rides an astronaut". This is because they fail to fully leverage structured knowledge when learning multi-modal representations. In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations. Firstly, we use scene graphs to guide the construction of semantic negative examples, which results in an increased emphasis on learning structured representations. Moreover, a Knowledge-Enhance Encoder (KEE) is proposed to leverage SGK as input to further enhance structured representations. To verify the effectiveness of the proposed framework, we pre-train our model with the aforementioned approaches and conduct experiments on downstream tasks. Experimental results demonstrate that Structure-CLIP achieves state-of-the-art (SOTA) performance on VG-Attribution and VG-Relation datasets, with 12.5% and 4.1% ahead of the multi-modal SOTA model respectively. Meanwhile, the results on MSCOCO indicate that Structure-CLIP significantly enhances the structured representations while maintaining the ability of general representations. Our code is available at https://github.com/zjukg/Structure-CLIP.

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IJCAI Conference 2022 Conference Paper

Dynamic Domain Generalization

Zhishu Sun
Zhifeng Shen
Luojun Lin
Yuanlong Yu
Zhifeng Yang
Shicai Yang
Weijie Chen

Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains. To tackle this problem, we develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in which the model learns to twist the network parameters to adapt to the data from different domains. Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains. In this way, the static model is optimized to learn domain-shared features, while the meta-adjuster is designed to learn domain-specific features. To enable this process, DomainMix is exploited to simulate data from diverse domains during teaching the meta-adjuster to adapt to the agnostic target domains. This learning mechanism urges the model to generalize to different agnostic target domains via adjusting the model without training. Extensive experiments demonstrate the effectiveness of our proposed method. Code is available: https: //github. com/MetaVisionLab/DDG

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JMLR Journal 2022 Journal Article

Fast and Robust Rank Aggregation against Model Misspecification

Yuangang Pan
Ivor W. Tsang
Weijie Chen
Gang Niu
Masashi Sugiyama

In rank aggregation (RA), a collection of preferences from different users are summarized into a total order under the assumption of homogeneity of users. Model misspecification in RA arises since the homogeneity assumption fails to be satisfied in the complex real-world situation. Existing robust RAs usually resort to an augmentation of the ranking model to account for additional noises, where the collected preferences can be treated as a noisy perturbation of idealized preferences. Since the majority of robust RAs rely on certain perturbation assumptions, they cannot generalize well to agnostic noise-corrupted preferences in the real world. In this paper, we propose CoarsenRank, which possesses robustness against model misspecification. Specifically, the properties of our CoarsenRank are summarized as follows: (1) CoarsenRank is designed for mild model misspecification, which assumes there exist the ideal preferences (consistent with model assumption) that locate in a neighborhood of the actual preferences. (2) CoarsenRank then performs regular RAs over a neighborhood of the preferences instead of the original data set directly. Therefore, CoarsenRank enjoys robustness against model misspecification within a neighborhood. (3) The neighborhood of the data set is defined via their empirical data distributions. Further, we put an exponential prior on the unknown size of the neighborhood and derive a much-simplified posterior formula for CoarsenRank under particular divergence measures. (4) CoarsenRank is further instantiated to Coarsened Thurstone, Coarsened Bradly-Terry, and Coarsened Plackett-Luce with three popular probability ranking models. Meanwhile, tractable optimization strategies are introduced with regards to each instantiation respectively. In the end, we apply CoarsenRank on four real-world data sets. Experiments show that CoarsenRank is fast and robust, achieving consistent improvements over baseline methods. [abs] [ pdf ][ bib ] &copy JMLR 2022. ( edit, beta )

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AAAI Conference 2021 Conference Paper

A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data

Xianfeng Li
Weijie Chen
Di Xie
Shicai Yang
Peng Yuan
Shiliang Pu
Yueting Zhuang

Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and trained together to reduce the domain gap. Considering the data privacy, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into learning with noisy labels. A straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation. Nonetheless, completely clean labels are still unattainable. After an experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.

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EAAI Journal 2020 Journal Article

Group decision making under generalized fuzzy soft sets and limited cognition of decision makers

Weijie Chen
Yan Zou

Typically, the decision making process assumes that the decision maker’s cognition for all aspects of a problem is the same. However, inadequate experience, lack of knowledge, and time suggest otherwise. Therefore, to recognize the impact of the decision maker’s cognition on the validity of the information provided, this paper develops a fuzzy group decision making method based on the generalized fuzzy soft set (GFSS). We apply the Bonferroni mean operators to develop the GFSS Bonferroni mean operator, which can be used for aggregating the information gleaned from the decision makers into collective information, and we construct the GFSS to revise the information provided by the decision makers (DMs). A similarity measure between the GFSSs is proposed and is used to identify the DMs’ weights. Finally, an illustrative example highlights the proposed method and demonstrates the solution characteristics.

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AAAI Conference 2020 Conference Paper

Neural Inheritance Relation Guided One-Shot Layer Assignment Search

Rang Meng
Weijie Chen
Di Xie
Yuan Zhang
Shiliang Pu

Layer assignment is seldom picked out as an independent research topic in neural architecture search. In this paper, for the ﬁrst time, we systematically investigate the impact of different layer assignments to the network performance by building an architecture dataset of layer assignment on CIFAR-100. Through analyzing this dataset, we discover a neural inheritance relation among the networks with different layer assignments, that is, the optimal layer assignments for deeper networks always inherit from those for shallow networks. Inspired by this neural inheritance relation, we propose an efﬁcient one-shot layer assignment search approach via inherited sampling. Speciﬁcally, the optimal layer assignment searched in the shallow network can be provided as a strong sampling priori to train and search the deeper ones in supernet, which extremely reduces the network search space. Comprehensive experiments carried out on CIFAR-100 illustrate the efﬁciency of our proposed method. Our search results are strongly consistent with the optimal ones directly selected from the architecture dataset. To further conﬁrm the generalization of our proposed method, we also conduct experiments on Tiny- ImageNet and ImageNet. Our searched results are remarkably superior to the handcrafted ones under the unchanged computational budgets. The neural inheritance relation discovered in this paper can provide insights to the universal neural architecture search.

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AAAI Conference 2019 Conference Paper

A Layer Decomposition-Recomposition Framework for Neuron Pruning towards Accurate Lightweight Networks

Weijie Chen
Yuan Zhang
Di Xie
Shiliang Pu

Neuron pruning is an efficient method to compress the network into a slimmer one for reducing the computational cost and storage overhead. Most of state-of-the-art results are obtained in a layer-by-layer optimization mode. It discards the unimportant input neurons and uses the survived ones to reconstruct the output neurons approaching to the original ones in a layer-by-layer manner. However, an unnoticed problem arises that the information loss is accumulated as layer increases since the survived neurons still do not encode the entire information as before. A better alternative is to propagate the entire useful information to reconstruct the pruned layer instead of directly discarding the less important neurons. To this end, we propose a novel Layer Decomposition- Recomposition Framework (LDRF) for neuron pruning, by which each layer’s output information is recovered in an embedding space and then propagated to reconstruct the following pruned layers with useful information preserved. We mainly conduct our experiments on ILSVRC-12 benchmark with VGG-16 and ResNet-50. What should be emphasized is that our results before end-to-end fine-tuning are significantly superior owing to the information-preserving property of our proposed framework. With end-to-end fine-tuning, we achieve state-of-the-art results of 5. 13× and 3× speed-up with only 0. 5% and 0. 65% top-5 accuracy drop respectively, which outperform the existing neuron pruning methods.

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IJCAI Conference 2019 Conference Paper

Attribute-Aware Convolutional Neural Networks for Facial Beauty Prediction

Luojun Lin
Lingyu Liang
Lianwen Jin
Weijie Chen

Facial beauty prediction (FBP) aims to develop a machine that automatically makes facial attractiveness assessment. To a large extent, the perception of facial beauty for a human is involved with the attributes of facial appearance, which provides some significant visual cues for FBP. Deep convolution neural networks (CNNs) have shown its power for FBP, but convolution filters with fixed parameters cannot take full advantage of the facial attributes for FBP. To address this problem, we propose an Attribute-aware Convolutional Neural Network (AaNet) that modulates the filters of the main network, adaptively, using parameter generators that take beauty-related attributes as extra inputs. The parameter generators update the filters in the main network in two different manners: filter tuning or filter rebirth. However, AaNet takes attributes information as prior knowledge, that is ill-suited to those datasets merely with task-oriented labels. Therefore, imitating the design of AaNet, we further propose a Pseudo Attribute-aware Convolutional Neural Network (P-AaNet) that modulates filters conditioned on global context embeddings (pseudo attributes) of input faces learnt by a lightweight pseudo attribute distiller. Extensive ablation studies show that the AaNet and P-AaNet improve the performance of FBP when compared to conventional convolution and attention scheme, which validates the effectiveness of our method.

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