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Zihao Chen

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10 papers

2 author rows

EAAI Journal 2026 Journal Article

Aircraft geomagnetic navigation via dual-view feature extraction and hybrid multi-criteria adaptive weighting

Yifan Li
Zihao Chen
Mingqi Lv
Tieming Chen
Baiyang Ji

Geomagnetic navigation is a passive technique that leverages the spatial distribution of the Earth’s magnetic field to mitigate the susceptibility of Global Navigation Satellite Systems (GNSS) to external interference and reduce cumulative errors in inertial navigation systems, thereby ensuring robust stability. However, its relatively low accuracy has historically limited practical deployment. To address this challenge, this paper proposes an aircraft geomagnetic navigation method via dual-view feature extraction and hybrid multi-criteria adaptive weighting (DHAGN). DHAGN extracts features from two distinct views, adaptively adjusts feature weights using both standard-deviation-based and summation-based criteria, and integrates an loss-feedback mechanism within the summation-based weighting to further enhance navigation accuracy. Experiments on 13 flight routes from the SGL2020 dataset demonstrate that DHAGN achieves an average distance-root-mean-square (DRMS) error reduction of 45. 5 meters compared to the state-of-the-art Magnav2C method, validating its effectiveness in enhancing geomagnetic navigation accuracy and facilitating practical implementation.

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EAAI Journal 2026 Journal Article

Multitasking optimization for personalized exercise group recommendation in E-learning environments

Haipeng Yang
Sibo Liu
Zihao Chen
Yuanyuan Ge
Lei Zhang

Personalized exercise group recommendation (PEGR) is to select a set of exercises from a large exercise bank for students, which plays an important role in E-learning. Due to the complexity of real application scenarios, PEGR is usually modeled as a large-scale constrained multi-objective optimization problem and solved by multi-objective evolutionary algorithms (MOEAs). However, the “curse of dimensionality” and the complex constraints handling are the two challenges encountered when designing MOEAs to solve the PEGR problem. To this end, we propose a novel evolutionary tri-tasking algorithm named ETT-PEGR to tackle the challenges of solving the PEGR, in which two auxiliary tasks are constructed to help solve the original task through knowledge transfer. Specifically, the first concept-recommended auxiliary task is designed to recommend knowledge concepts instead of exercises to students, which can help accelerate the convergence speed of the original task since the number of concepts is much smaller than that of exercises. The second constraint-ignored auxiliary task is designed to help the solutions of the original task to cross the infeasible region. In addition, a novel knowledge transfer mechanism based on different encoding strategies is proposed for the original task and the two auxiliary tasks, which can effectively realize the knowledge transfer between them. Experimental results on four popular datasets show that ETT-PEGR outperforms the state-of-the-art algorithms for PEGR.

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AAAI Conference 2026 Conference Paper

NODiff: Neural Operator Diffusion for Multispectral Image Fusion

Junming Hou
Ran Ran
Sixing Chen
Zihao Chen
Xiaofeng Cong
Junling Li
Liang-Jian Deng

Pansharpening is a powerful technique for generating high-resolution multispectral (HRMS) images by fusing currently available image pairs of low-resolution multispectral (LRMS) and texture-rich panchromatic (PAN) data, effectively addressing the physical constraints of satellite sensors. While recent generative diffusion models have demonstrated impressive performance gains in this domain, their prohibitive computational demands and training costs hinder practicality in resource-constrained remote sensing satellite systems. In this work, we propose NODiff, a novel diffusion framework that replaces the conventional attention-based denoising backbone with a neural operator, seamlessly integrating operator learning and generative modeling into an efficient yet effective solution for pansharpening. In practice, we implement our approach through a two-stage learning paradigm: First, we pretrain the proposed Neural Operator-based diffusion model to learn the high-resolution texture priors essential for pansharpening. Afterward, we freeze the pretrained parameters, and design a lightweight conditional detail guidance adapter to enable efficient fine-tuning for generating desired HRMS images. Meanwhile, a time-aware low-rank adaptation is introduced to dynamically refine high-frequency details potentially affected by spectral mode truncation. Extensive experiments on multiple benchmark datasets demonstrate that NODiff achieves competitive pansharpening performance while significantly reducing training and inference costs. Beyond pansharpening, our method provides new insights into building resource-efficient generative models.

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IROS Conference 2025 Conference Paper

ATARS: An Aerial Traffic Atomic Activity Recognition and Temporal Segmentation Dataset

Zihao Chen
Hsuanyu Wu
Chi-Hsi Kung
Yiting Chen
Yan-Tsung Peng

Traffic Atomic Activity, which describes traffic patterns for topological intersection dynamics, is a crucial topic for the advancement of intelligent driving systems. However, existing atomic activity datasets are collected from an egocentric view, which cannot support the scenarios where traffic activities in an entire intersection are required. Moreover, existing datasets only provide video-level atomic activity annotations, which require exhausting efforts to manually trim the videos for recognition and limit their applications to untrimmed videos. To bridge this gap, we introduce the Aerial Traffic Atomic Activity Recognition and Segmentation (ATARS) dataset, the first aerial dataset designed for multilabel atomic activity analysis. We offer atomic activity labels for each frame, which accurately record the intervals for traffic activities. Moreover, we propose a novel task, Multi-label Temporal Atomic Activity Recognition, enabling the study of accurate temporal localization for atomic activity and easing the burden of manual video trimming for recognition. We conduct extensive experiments to evaluate existing state-of-theart models on both atomic activity recognition and temporal atomic activity segmentation. The results highlight the unique challenges of our ATARS dataset, such as recognizing extremely small objects’ activities. We further provide a comprehensive discussion analyzing these challenges and offer valuable insights for future direction to improve recognition of atomic activity in an aerial view. Our source code and dataset are available at https://github.com/magecliff96/ATARS/.

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AIJ Journal 2025 Journal Article

MATE: Masked optimal transport with dynamic selection for partial label graph learning

Yiyang Gu
Binqi Chen
Zihao Chen
Ziyue Qiao
Xiao Luo
Junyu Luo
Zhiping Xiao
Wei Ju

This paper investigates the problem of partial label graph learning, in which every graph is associated with a set of candidate labels. Previous methods for weakly supervised graph classification often provide pseudo-labels for graph samples that could be overconfident and biased towards the dominant classes, thus resulting in substantial error accumulation. In this paper, we introduce a new framework named Masked Optimal Transport with Dynamic Selection (MATE) for partial label graph learning, which improves the quality of graph assignments from the perspectives of class balancing and uncertainty mining. In particular, our MATE masks probabilities out of candidate sets and then adopts optimal transport to optimize the assignments without class biases. This design is based on the assumption that the true label distribution is class-balanced or nearly balanced, which is common in various training datasets and real-world scenarios. To further reduce potential noise, we propose a novel scoring metric termed partial energy discrepancy (PED) to evaluate the uncertainty of assignments, and then introduce a dynamic selection strategy that modifies the sample-specific thresholds via momentum updating. Finally, these samples are divided into three levels, i. e. , confident, less-confident, and unconfident and each group is trained separately in our collaborative optimization framework. Extensive experiments on various benchmarks demonstrate the superiority of our MATE compared to various state-of-the-art baselines.

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NeurIPS Conference 2025 Conference Paper

Physics-informed Neural Operator for Pansharpening

Xinyang Liu
Junming Hou
Chenxu Wu
Xiaofeng Cong
Zihao Chen
Shangqi Deng
Junling Li
Liang-Jian Deng

Over the past decades, pansharpening has contributed greatly to numerous remote sensing applications, with methods evolving from theoretically grounded models to deep learning approaches and their hybrids. Though promising, existing methods rarely address pansharpening through the lens of underlying physical imaging processes. In this work, we revisit the spectral imaging mechanism and propose a novel physics‐informed neural operator framework for pansharpening, termed PINO, which faithfully models the end‐to‐end electro‐optical sensor process. Specifically, PINO operates as: (1) First, a spatial-spectral encoder pair is introduced to aggregate multi-granularity high-resolution panchromatic (PAN) and low-resolution multispectral (LRMS) features. (2) Subsequently, an iterative neural integral process utilizes these fused spatial-spectral characteristics to learn a continuous radiance field $L_i(x, y, \lambda)$ over spatial coordinates and wavelength, effectively emulating band-wise spectral integration. (3) Finally, the learned radiance field is modulated by the sensor’s spectral responsivity $R_b(\lambda)$ to produce physically consistent spatial–spectral fusion products. This physics-grounded fusion paradigm offers a principled solution for reconstructing high-resolution multispectral and hyperspectral images in accordance with sensor imaging physics, effectively harnessing the unique advantages of spectral data to better uncover real-world characteristics. Experiments on multiple benchmark datasets show that our method surpasses state-of-the-art fusion algorithms, achieving reduced spectral aberrations and finer spatial textures. Furthermore, extension to hyperspectral (HS) data demonstrates its generalizability and universality. The code will be available upon potential acceptance.

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YNIMG Journal 2024 Journal Article

Elucidating genetic and molecular basis of altered higher-order brain structure-function coupling in major depressive disorder

Haixia Long
Zihao Chen
Xinli Xu
Qianwei Zhou
Zhaolin Fang
Mingqi Lv
Xu-Hua Yang
Jie Xiao

Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/β-hydrolase domain-containing 6 (ABHD6), β 1,3-N-acetylglucosaminyltransferase-9(β3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, β3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.

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NeurIPS Conference 2024 Conference Paper

Semi-supervised Knowledge Transfer Across Multi-omic Single-cell Data

Fan Zhang
Tianyu Liu
Zihao Chen
Xiaojiang Peng
Chong Chen
Xian-Sheng Hua
Xiao Luo
Hongyu Zhao

Knowledge transfer between multi-omic single-cell data aims to effectively transfer cell types from scRNA-seq data to unannotated scATAC-seq data. Several approaches aim to reduce the heterogeneity of multi-omic data while maintaining the discriminability of cell types with extensive annotated data. However, in reality, the cost of collecting both a large amount of labeled scRNA-seq data and scATAC-seq data is expensive. Therefore, this paper explores a practical yet underexplored problem of knowledge transfer across multi-omic single-cell data under cell type scarcity. To address this problem, we propose a semi-supervised knowledge transfer framework named Dual label scArcity elimiNation with Cross-omic multi-samplE Mixup (DANCE). To overcome the label scarcity in scRNA-seq data, we generate pseudo-labels based on optimal transport and merge them into the labeled scRNA-seq data. Moreover, we adopt a divide-and-conquer strategy which divides the scATAC-seq data into source-like and target-specific data. For source-like samples, we employ consistency regularization with random perturbations while for target-specific samples, we select a few candidate labels and progressively eliminate incorrect cell types from the label set for additional supervision. Next, we generate virtual scRNA-seq samples with multi-sample Mixup based on the class-wise similarity to reduce cell heterogeneity. Extensive experiments on many benchmark datasets suggest the superiority of our DANCE over a series of state-of-the-art methods.

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NeurIPS Conference 2024 Conference Paper

Your contrastive learning problem is secretly a distribution alignment problem

Zihao Chen
Chi-Heng Lin
Ran Liu
Jingyun Xiao
Eva L. Dyer

Despite the success of contrastive learning (CL) in vision and language, its theoretical foundations and mechanisms for building representations remain poorly understood. In this work, we build connections between noise contrastive estimation losses widely used in CL and distribution alignment with entropic optimal transport (OT). This connection allows us to develop a family of different losses and multistep iterative variants for existing CL methods. Intuitively, by using more information from the distribution of latents, our approach allows a more distribution-aware manipulation of the relationships within augmented sample sets. We provide theoretical insights and experimental evidence demonstrating the benefits of our approach for generalized contrastive alignment. Through this framework, it is possible to leverage tools in OT to build unbalanced losses to handle noisy views and customize the representation space by changing the constraints on alignment. By reframing contrastive learning as an alignment problem and leveraging existing optimization tools for OT, our work provides new insights and connections between different self-supervised learning models in addition to new tools that can be more easily adapted to incorporate domain knowledge into learning.

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AAAI Conference 2017 Conference Paper

Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features

Zihao Chen
Luo Luo
Zhihua Zhang

Recently, there has been an increasing interest in designing distributed convex optimization algorithms under the setting where the data matrix is partitioned on features. Algorithms under this setting sometimes have many advantages over those under the setting where data is partitioned on samples, especially when the number of features is huge. Therefore, it is important to understand the inherent limitations of these optimization problems. In this paper, with certain restrictions on the communication allowed in the procedures, we develop tight lower bounds on communication rounds for a broad class of non-incremental algorithms under this setting. We also provide a lower bound on communication rounds for a class of (randomized) incremental algorithms.

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