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Lihui Chen

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

CLIPPan: Adapting CLIP as a Supervisor for Unsupervised Pansharpening

Lihua Jian
Jiabo Liu
Shaowu Wu
Lihui Chen

Despite remarkable advancements in supervised pansharpening neural networks, these methods face domain adaptation challenges of resolution due to the intrinsic disparity between simulated reduced-resolution training data and real-world full-resolution scenarios. To bridge this gap, we propose an unsupervised pansharpening framework, CLIPPan, that enables model training at full resolution directly by taking CLIP, a visual-language model, as a supervisor. However, directly applying CLIP to supervise pansharpening remains challenging due to its inherent bias toward natural images and limited understanding of pansharpening tasks. Therefore, we first introduce a lightweight fine-tuning pipeline that adapts CLIP to recognize low-resolution multispectral, panchromatic, and high-resolution multispectral images, as well as to understand the pansharpening process. Then, building on the adapted CLIP, we formulate a novel loss integrating semantic language constraints, which aligns image-level fusion transitions with protocol-aligned textual prompts (e.g., Wald's or Khan's descriptions), thus enabling CLIPPan to use language as a powerful supervisory signal and guide fusion learning without ground truth. Extensive experiments demonstrate that CLIPPan consistently improves spectral and spatial fidelity across various pansharpening backbones on real-world datasets, setting a new state of the art for unsupervised full-resolution pansharpening.

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EAAI Journal 2026 Journal Article

Multiscale wavelet-based spatial–spectral compression network for hyperspectral image

Hang Yu
Mingyang Wan
Tao Chen
Aibin Peng
Xiangfei Shen
Rulong He
Lihui Chen
Haijun Liu

Hyperspectral images (HSIs) possess high-dimensional tensor structures that present significant reconstruction challenges under ultra-low compression ratios (CR) in artificial intelligence-driven remote sensing. Conventional compression methods are unable to effectively capture inherent spatial–spectral coherence and often neglect multiscale spectral absorption-reflection dependencies, which are critical for maintaining spectral fidelity. To overcome these shortcomings, we propose a Multiscale Wavelet-based Spatial-Spectral Compression Network (MWC-Net) for HSI reconstruction. Methodologically, MWC-Net integrates a three-dimensional (3D) spatial–spectral attention encoder, which via tri-branch attention to extract complete spatial–spectral coherence. Additionally, we develop a multiscale wavelet spatial–spectral decoder that restores scale-sensitive spectral features through multiscale super-resolution and enhances spatial–spectral resolution using wavelet decomposition. Compared to the state-of-the-art method “Hyperspectral Image Compression Sensing Network With Convolutional Neural Networks (CNN)–Transformer Mixture Architectures”, MWC-Net achieves an average decrease from 1. 770 to 1. 549 in the spectral angle mapper (SAM) metric. Additionally, the average peak signal-to-noise ratio (PSNR) increases from 39. 81 to 40. 79, while the average root mean square error (RMSE) decreases from 55. 15 to 49. 97, under approximately 1% CR. This enhancement highlights the superior ability of MWC-Net to balance compression efficiency and spectral fidelity in HSI reconstruction. The code can be available on https: //github. com/YuHang-max/MWCNet.

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AAAI Conference 2025 Conference Paper

EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution

Xi Su
Xiangfei Shen
Mingyang Wan
Jing Nie
Lihui Chen
Haijun Liu
Xichuan Zhou

Single hyperspectral image super-resolution (single-HSI-SR) aims to improve the resolution of a single input low-resolution HSI. Due to the bottleneck of data scarcity, the development of single-HSI-SR lags far behind that of RGB natural images. In recent years, research on RGB SR has shown that models pre-trained on large-scale benchmark datasets can greatly improve performance on unseen data, which may stand as a remedy for HSI. But how can we transfer the pre-trained RGB model to HSI, to overcome the data-scarcity bottleneck? Because of the significant difference in the channels between the pre-trained RGB model and the HSI, the model cannot focus on the correlation along the spectral dimension, thus limiting its ability to utilize on HSI. Inspired by the HSI spatial-spectral decoupling, we propose a new framework that first fine-tunes the pre-trained model with the spatial components (known as eigenimages), and then infers on unseen HSI using an iterative spectral regularization (ISR) to maintain the spectral correlation. The advantages of our method lie in: 1) we effectively inject the spatial texture processing capabilities of the pre-trained RGB model into HSI while keeping spectral fidelity, 2) learning in the spectral-decorrelated domain can improve the generalizability to spectral-agnostic data, and 3) our inference in the eigenimage domain naturally exploits the spectral low-rank property of HSI, thereby reducing the complexity. This work bridges the gap between pre-trained RGB models and HSI via eigenimages, addressing the issue of limited HSI training data, hence the name EigenSR. Extensive experiments show that EigenSR outperforms the state-of-the-art (SOTA) methods in both spatial and spectral metrics.

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EAAI Journal 2025 Journal Article

Hybrid cross-modality fusion network for medical image segmentation with contrastive learning

Xichuan Zhou
Qianqian Song
Jing Nie
Yujie Feng
Haijun Liu
Fu Liang
Lihui Chen
Jin Xie

Medical image segmentation has been widely adopted in artificial intelligence-based clinical applications. The integration of medical texts into image segmentation models has significantly improved the segmentation performance. It is crucial to design an effective fusion manner to integrate the paired image and text features. Existing multi-modal medical image segmentation methods fuse the paired image and text features through a non-local attention mechanism, which lacks local interaction. Besides, they lack a mechanism to enhance the relevance of the paired features and keep the discriminability of unpaired features in the training process, which limits the segmentation performance. To solve the above problem, we propose a hybrid cross-modality fusion network (HCFNet) based on contrastive learning for medical image segmentation. The key designs of our proposed method are a multi-stage cross-modality contrastive loss and a hybrid cross-modality feature decoder. The multi-stage cross-modality contrastive loss is utilized to enhance the discriminability of the paired features and separate the unpaired features. Furthermore, the hybrid cross-modality feature decoder conducts local and non-local cross-modality feature interaction by a local cross-modality fusion module and a non-local cross-modality fusion module, respectively. Experimental results show that our method achieved state-of-the-art results on two public medical image segmentation datasets.

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ICLR Conference 2025 Conference Paper

Let Your Features Tell The Differences: Understanding Graph Convolution By Feature Splitting

Yilun Zheng
Xiang Li
Sitao Luan
Xiaojiang Peng
Lihui Chen

Graph Neural Networks (GNNs) have demonstrated strong capabilities in processing structured data. While traditional GNNs typically treat each feature dimension equally important during graph convolution, we raise an important question: **Is the graph convolution operation equally beneficial for each feature?** If not, the convolution operation on certain feature dimensions can possibly lead to harmful effects, even worse than convolution-free models. Therefore, it is required to distinguish convolution-favored and convolution-disfavored features. Traditional feature selection methods mainly focus on identifying informative features or reducing redundancy, but they are not suitable for structured data as they overlook graph structures. In graph community, some studies have investigated the performance of GNN with respect to node features using feature homophily metrics, which assess feature consistency across graph topology. Unfortunately, these metrics do not effectively align with GNN performance and cannot be reliably used for feature selection in GNNs. To address these limitations, we introduce a novel metric, Topological Feature Informativeness (TFI), to distinguish GNN-favored and GNN-disfavored features, where its effectiveness is validated through both theoretical analysis and empirical observations. Based on TFI, we propose a simple yet effective Graph Feature Selection (GFS) method, which processes GNN-favored and GNN-disfavored features with GNNs and non-GNN models separately. Compared to original GNNs, GFS significantly improves the extraction of useful topological information from each feature with comparable computational costs. Extensive experiments show that after applying GFS to $\textbf{8}$ baseline and state-of-the-art (SOTA) GNN architectures across $\textbf{10}$ datasets, $\textbf{90\%}$ of the GFS-augmented cases show significant performance boosts. Furthermore, our proposed TFI metric outperforms other feature selection methods for GFS. These results verify the effectiveness of both GFS and TFI. Additionally, we demonstrate that GFS's improvements are robust to hyperparameter tuning, highlighting its potential as a universally valid method for enhancing various GNN architectures. To facilitate reproducibility and further research, we have made our code publicly available at https://github.com/KTTRCDL/graph-feature-selection.

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JBHI Journal 2025 Journal Article

MIT-SAM: Medical Image-Text SAM With Mutually Enhanced Heterogeneous Features Fusion for Medical Image Segmentation

Xichuan Zhou
Lingfeng Yan
Rui Ding
Chukwuemeka Clinton Atabansi
Jing Nie
Lihui Chen
Yujie Feng
Haijun Liu

In recent times, leveraging lesion text as supplementary data to enhance the performance of medical image segmentation models has garnered attention. Previous approaches only used attention mechanisms to integrate image and text features, while not effectively utilizing the highly condensed textual semantic information in improving the fused features, resulting in inaccurate lesion segmentation. This paper introduces a novel approach, the Medical Image-Text Segment Anything Model (MIT-SAM), for text-assisted medical image segmentation. Specifically, we introduce the SAM-enhanced image encoder and a Bert-based text encoder to extract heterogeneous features. To better leverage the highly condensed textual semantic information for heterogeneous feature fusion, such as crucial details like position and quantity, we propose the image-text interactive fusion (ITIF) block and self-supervised text reconstruction (SSTR) method. The ITIF block facilitates the mutual enhancement of homogeneous information among heterogeneous features and the SSTR method empowers the model to capture crucial details concerning lesion text, including location, quantity, and other key aspects. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on the QaTa-COV19 and MosMedData+ datasets.

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NeurIPS Conference 2024 Conference Paper

What Is Missing For Graph Homophily? Disentangling Graph Homophily For Graph Neural Networks

Yilun Zheng
Sitao Luan
Lihui Chen

Graph homophily refers to the phenomenon that connected nodes tend to share similar characteristics. Understanding this concept and its related metrics is crucial for designing effective Graph Neural Networks (GNNs). The most widely used homophily metrics, such as edge or node homophily, quantify such "similarity" as label consistency across the graph topology. These metrics are believed to be able to reflect the performance of GNNs, especially on node-level tasks. However, many recent studies have empirically demonstrated that the performance of GNNs does not always align with homophily metrics, and how homophily influences GNNs still remains unclear and controversial. Then, a crucial question arises: What is missing in our current understanding of homophily? To figure out the missing part, in this paper, we disentangle the graph homophily into three aspects: label, structural, and feature homophily, which are derived from the three basic elements of graph data. We argue that the synergy of the three homophily can provide a more comprehensive understanding of GNN performance. Our new proposed structural and feature homophily consider the neighborhood consistency and feature dependencies among nodes, addressing the previously overlooked structural and feature aspects in graph homophily. To investigate their synergy, we propose a Contextual Stochastic Block Model with three types of Homophily (CSBM-3H), where the topology and feature generation are controlled by the three metrics. Based on the theoretical analysis of CSBM-3H, we derive a new composite metric, named Tri-Hom, that considers all three aspects and overcomes the limitations of conventional homophily metrics. The theoretical conclusions and the effectiveness of Tri-Hom have been verified through synthetic experiments on CSBM-3H. In addition, we conduct experiments on $31$ real-world benchmark datasets and calculate the correlations between homophily metrics and model performance. Tri-Hom has significantly higher correlation values than $17$ existing metrics that only focus on a single homophily aspect, demonstrating its superiority and the importance of homophily synergy. Our code is available at https: //github. com/zylMozart/Disentangle_GraphHom.

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AIIM Journal 2020 Journal Article

A trusted medical image super-resolution method based on feedback adaptive weighted dense network

Lihui Chen
Xiaomin Yang
Gwanggil Jeon
Marco Anisetti
Kai Liu

High-resolution (HR) medical images are preferred in clinical diagnoses and subsequent analysis. However, the acquisition of HR medical images is easily affected by hardware devices. As an effective and trusted alternative method, the super-resolution (SR) technology is introduced to improve the image resolution. Compared with traditional SR methods, the deep learning-based SR methods can obtain more clear and trusted HR images. In this paper, we propose a trusted deep convolutional neural network-based SR method named feedback adaptive weighted dense network (FAWDN) for HR medical image reconstruction. Specifically, the proposed FAWDN can transmit the information of the output image to the low-level features by a feedback connection. To explore advanced feature representation and reduce the feature redundancy in dense blocks, an adaptive weighted dense block (AWDB) is introduced to adaptively select the informative features. Experimental results demonstrate that our FAWDN outperforms the state-of-the-art image SR methods and can obtain more clear and trusted medical images than comparative methods.

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IJCAI Conference 2017 Conference Paper

Multiple Medoids based Multi-view Relational Fuzzy Clustering with Minimax Optimization

Yangtao Wang
Lihui Chen
Xiao-li Li

Multi-view data becomes prevalent nowadays because more and more data can be collected from various sources. Each data set may be described by different set of features, hence forms a multi-view data set or multi-view data in short. To find the underlying pattern embedded in an unlabelled multi-view data, many multi-view clustering approaches have been proposed. Fuzzy clustering in which a data object can belong to several clusters with different memberships is widely used in many applications. However, in most of the fuzzy clustering approaches, a single center or medoid is considered as the representative of each cluster in the end of clustering process. This may not be sufficient to ensure accurate data analysis. In this paper, a new multi-view fuzzy clustering approach based on multiple medoids and minimax optimization called M4-FC for relational data is proposed. In M4-FC, every object is considered as a medoid candidate with a weight. The higher the weight is, the more likely the object is chosen as the final medoid. In the end of clustering process, there may be more than one mediod in each cluster. Moreover, minimax optimization is applied to find consensus clustering results of different views with its set of features. Extensive experimental studies on several multi-view data sets including real world image and document data sets demonstrate that M4-FC not only outperforms single medoid based multi-view fuzzy clustering approach, but also performs better than existing multi-view relational clustering approaches.

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