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Xiong Yang

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4 papers

2 author rows

ICRA Conference 2025 Conference Paper

A Highly Robust Contact Sensor for Precise Contact Detection of Fabric

Zhengrong Ling
Lanxuan Hong
Xiong Yang
Yifeng Tang
Dong Guo
Yajing Shen

Automation in the apparel and textile industry has long been a pursuit. However, accurately locating the surface of a fabric remains a challenge, limiting the automation in sorting, packaging, and other processes. When humans locate clothing, they rely on contact feedback for the exact position of the clothing surface. As existing contact detection solutions are significantly affected by environmental factors, it is essential to develop a sensor with robust contact detection capabilities. In this work, we introduce a contact sensor with high robustness and high force resolution. This contact sensor detects contact by measuring the deformation of an elastomer using a distancemeasuring module. Based on the deformation characteristics of the elastomer, we designed a detection algorithm that not only reduces the noise of data but also extracts features such as trends and elastomer states, enabling reliable contact detection. Through experiments, we validated that this contact sensor can detect contact forces as low as 0. 017 N and is robust to external interference or sensor movement. We also verified that the sensor can process data within 7. 5 ms and return contact detection with 95% accuracy. Additionally, we assessed its effectiveness in real fabric contact scenarios.

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AAAI Conference 2025 Conference Paper

Debiased Distillation for Consistency Regularization

Lu Wang
Liuchi Xu
Xiong Yang
Zhenhua Huang
Jun Cheng

Knowledge distillation transfers "dark knowledge" from a large teacher model to a smaller student model, yielding a highly efficient network. To improve network's generalization ability, existing works use a larger temperature coefficient for knowledge distillation. Nevertheless, these methods may lower the target category's confidence and lead to ambiguous recognition of similar samples. To mitigate this issue, some studies introduce intra-batch distillation to reduce prediction discrepancy. However, these methods overlook the inconsistency between background information and the target category, which may increase prediction bias due to noise disturbance. Additionally, label imbalance from random sampling and batch size can undermine network generalization reliability. To tackle these challenges, we propose a simple yet effective Intra-class Knowledge Distillation (IKD) method that facilitates knowledge sharing within the same class to ensure consistent predictions. First, we initialize the matrix and the vector to store logits and class counts provided by the teacher, respectively. Then, in the first epoch, we calculate the sum of logits and sample counts per class and perform KD to prevent knowledge omission. Finally, in subsequent training, we update the matrix to obtain the average logits and compute the KL divergence between the student's output and the updated matrix according to the label index. This process ensures intra-class consistency and improves the student's performance. Furthermore, this method theoretically reduces prediction bias by ensuring intra-class consistency. Extensive experiments on the CIFAR-100, ImageNet-1K, and Tiny-ImageNet datasets validate the superiority of IKD.

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JBHI Journal 2025 Journal Article

Unsupervised Domain Adaptation With Synchronized Self-Training for Cross- Domain Motor Imagery Recognition

Peiyin Chen
Xiaofeng Liu
Chao Ma
He Wang
Xiong Yang
Celso Grebogi
Xiao Gu
Zhongke Gao

Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often rely on large amounts of annotated data collected through specific experimental setups, which fail to address the heterogeneity of data distributions across different domains. This limitation hinders BCI systems from effectively managing the complexity and variability of real-world data. To overcome these challenges, we propose Synchronized Self-Training Domain Adaptation (SSTDA) for cross-domain motor imagery classification. Specifically, SSTDA leverages labeled signals from a source domain and applies self-training to unlabeled signals from a target domain, enabling the simultaneous training of a more robust classifier. The raw EEG signals are mapped into a latent space by a feature extractor for discriminative representation learning. A domain-shared latent space is then learned by optimizing the feature extractor with both source and target samples, using an easy-tohard self-training process. We validate the method with extensive experiments on two public motor imagery datasets: Dataset IIa of BCI Competition IV and the High Gamma dataset. In the inter-subject task, our method achieves classification accuracies of 64. 43% and 80. 40%, respectively. It also outperforms existing methods in the inter-session task. Moreover, we develope a new six-class motor imagery dataset and achieve test accuracies of 77. 09% and 80. 18% across different datasets. All experimental results demonstrate that our SSTDA outperforms existing algorithms in inter-session, inter-subject, and inter-dataset validation protocols, highlighting its capability to learn discriminative, domain-invariant representations that enhance EEG decoding performance.

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IJCAI Conference 2020 Conference Paper

Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification

Lianwei Wu
Yuan Rao
Xiong Yang
Wanzhen Wang
Ambreen Nazir

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results.

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