Author name cluster

Han Han

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5 papers

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AAAI Conference 2026 Conference Paper

E-MaT:Event-oriented Mamba for Egocentric Point Tracking

Han Han
Wei Zhai
Baocai Yin
Yang Cao
Bin Li
Zheng-Jun Zha

Egocentric point tracking aims to localize points on object surfaces from a first-person perspective and serves as a critical step toward embodied intelligence. Recent methods rely on video input, tracking query points through feature matching across consecutive frames. However, these methods struggle in highly dynamic settings—a common challenge in first-person perspectives, where the head-mounted camera undergoes frequent and abrupt rotations, resulting in high angular velocities, motion blur, and large inter-frame displacements. In contrast, event cameras capture motion at microsecond temporal resolution, naturally avoiding blur and delivering low-latency, high-fidelity cues crucial for egocentric point tracking. Moreover, rapid egocentric motion disrupts local smoothness, breaking the assumption that spatially adjacent regions share similar motion. Event dynamics expose global motion trends, guiding coherent modeling and consistent feature flow. Therefore, this paper proposes a mamba-based tracking framework that constructs feature modeling paths aligned with the dominant motion trend extracted from events, and modulates feature propagation along these paths based on local motion intensity, enhancing stability by suppressing unreliable signals and emphasizing consistent cues. Additionally, a motion-adaptive suppression module enhances temporal robustness by adaptively suppressing correlation features based on motion intensity variations, mitigating the effects of intensity fluctuations and partial observability. To facilitate research in this domain, a multimodal dataset named DVS-EgoPoints with both events and videos for egocentric point tracking is collected. Experiments on the DVS-EgoPoints dataset and a simulation benchmark demonstrate superior performance over state-of-the-art methods, especially under challenging motion and occlusion conditions.

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JBHI Journal 2026 Journal Article

EQA-MDL: Wearable ECG Signal Quality Assessment via Multi-scale Difference Learning

Haoyi Fan
Jiawei Luo
Huihui Chang
Han Han
Jian Tan
Chunyi Guo
Muwei Li
Zongmin Wang

Signal quality assessment (SQA) is crucial in data collection and analysis based on wearable devices. However, most existing electrocardiogram (ECG) quality assessment methods rely on labeled data and struggle to accurately locate noisy areas. To address this issue, we propose a self-supervised method based on multi-scale difference learning, called EQA-MDL, which utilizes a noise generation module to provide pseudo-anomaly samples for difference learning and combines a reconstruction framework with multi-scale difference learning to capture key features for accurate anomaly localization. Specifically, we first design an ECG noise generation method to simulate real-world noise scenarios, thereby generating corresponding pseudo-anomalous samples along with their pixel-level labels. Second, a reconstruction-based self-supervised framework is introduced that leverages pseudo-anomalies to adversarially learn optimal latent feature representations of clean ECG signals. Finally, we design a multi-scale difference learning function to amplify the representational differences between noisy and high-quality ECG features, while incorporating representational correlations from the observation space into the feature space, thereby extracting more robust features. Extensive experiments conducted on two public datasets and three private datasets demonstrate that the proposed method significantly outperforms state-of-the-art approaches.

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JBHI Journal 2026 Journal Article

Pseudo Anomalies and Hard Sample Mining for Ventricular Arrhythmia Anomaly Detection

Haoyi Fan
Shaochen Gao
Han Han
Mingzhe Han
Bing Zhou
Zongmin Wang

Ventricular arrhythmias (VA) are among the most prevalent and clinically significant cardiac arrhythmias. Conventional detection methodologies predominantly employ supervised learning approaches that depend on precisely annotated training datasets. However, the morphological similarity between VA waveforms and noise artifacts poses a significant challenge for traditional algorithms in discriminating these clinically distinct categories. In this paper, we propose a novel anomaly detection framework named PHVA ( P seudo-data and H ard-sample mining for VA detection) based on one-class anomaly detection, where only normal ECGs are used for training, while pseudo-anomalies are generated through self-supervised modules. These modules, including pseudo-anomaly generation and hard-sample mining, provide supervisory signals without requiring abnormal labels. Specifically, we introduce a physiology-aware synthetic ECG generation method that captures the beat morphology and waveform characteristics of VA while incorporating realistic noise simulations based on conventional noise models. These pseudo-annotated signals are then used to refine the decision boundary of a time-frequency hypersphere, constructed from normal ECG features in both temporal and spectral domains. Additionally, we employ triplet-loss-based hard-sample mining to improve the model's discriminative power for ventricular fibrillation detection. Extensive experiments on three public ECG datasets demonstrate that our proposed method PHVA achieves superior overall performance, outperforming state-of-the-art anomaly detection baselines by up to 8. 7% in AUC (the Area Under a Receiver Operating Characteristic Curve). We share an online repository containing the code and data processing scripts https://github.com/Augustgaoshaochen/PHVA

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NeurIPS Conference 2025 Conference Paper

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization

Kuan Zhang
Chengliang Chai
Jingzhe Xu
Chi Zhang
Han Han
Ye Yuan
Guoren Wang
Lei Cao

Recent studies indicate that deep neural networks degrade in generalization performance under noisy supervision. Existing methods focus on isolating clean subsets or correcting noisy labels, facing limitations such as high computational costs, heavy hyperparameter tuning process, and coarse-grained optimization. To address these challenges, we propose a novel two-stage noisy learning framework that enables instance-level optimization through a dynamically weighted loss function, avoiding hyperparameter tuning. To obtain stable and accurate information about noise modeling, we introduce a simple yet effective metric, termed $\textit{wrong event}$, which dynamically models the cleanliness and difficulty of individual samples while maintaining computational costs. Our framework first collects $\textit{wrong event}$ information and builds a strong base model. Then we perform noise-robust training on the base model, using a probabilistic model to handle the $\textit{wrong event}$ information of samples. Experiments on six synthetic and real-world LNL benchmarks demonstrate our method surpasses state-of-the-art methods in performance, achieves a nearly 75\% reduction in storage and computational time, strongly improving model scalability. Our code is available at https: //github. com/iTheresaApocalypse/IDO.

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JBHI Journal 2025 Journal Article

Multimodal Time-Frequency Pseudo Anomalies for Atrial Fibrillation Anomaly Detection

Haoyi Fan
Han Han
Huihui Chang
Zongmin Wang

Atrial fibrillation anomaly detection is increasingly significant today as the incidence of cardiovascular disease continues to rise. However, most of the existing supervised learning based methods for computer-aided diagnosis of atrial fibrillation heavily rely on labeled data, which is not applicable because of the scarcity of atrial fibrillation ECG data. While unsupervised methods training solely with normal samples may result in blurred decision boundaries and inadequate discriminability. In this paper, we propose a method for atrial fibrillation anomaly detection based on multimodal time-frequency pseudo anomalies, which learns pseudo anomalies rectified time-frequency hypersphere under better ECG representations. Specifically, we propose an atrial fibrillation ECG generation method that considers the rhythm and wave characteristics to construct pseudo anomalies ECG signals. These pseudo anomalies signals are then used to optimize the time-frequency hypersphere boundary, which is learned from the features of normal ECG signals in both time and frequency domains, leading to more effective atrial fibrillation anomaly detection. Extensive experiments have been conducted on multiple ECG datasets to validate the effectiveness of the proposed method.

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