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Dapeng Tao

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12 papers

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AAAI Conference 2026 Conference Paper

ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs

Weigang Lu
Ziyu Guan
Wei Zhao
Yaming Yang
Yujie Sun
Zheng Liang
Yibing Zhan
Dapeng Tao

GNN-to-MLP (G2M) methods have emerged as a promising approach to accelerate Graph Neural Networks (GNNs) by distilling their knowledge into simpler Multi-Layer Perceptrons (MLPs). These methods bridge the gap between the expressive power of GNNs and the computational efficiency of MLPs, making them well-suited for resource-constrained environments. However, existing G2M methods are limited by their inability to flexibly adjust inference cost and accuracy dynamically, a critical requirement for real-world applications where computational resources and time constraints can vary significantly. To address this, we introduce a Progressive framework designed to offer flexible and on-demand trade-offs between inference cost and accuracy for GNN-to-MLP knowledge distillation (ProGMLP). ProGMLP employs a Progressive Training Structure (PTS), where multiple MLP students are trained in sequence, each building on the previous one. Furthermore, ProGMLP incorporates Progressive Knowledge Distillation (PKD) to iteratively refine the distillation process from GNNs to MLPs, and Progressive Mixup Augmentation (PMA) to enhance generalization by progressively generating harder mixed samples. Our approach is validated through comprehensive experiments on eight real-world graph datasets, demonstrating that ProGMLP maintains high accuracy while dynamically adapting to varying runtime scenarios, making it highly effective for deployment in diverse application settings.

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AAAI Conference 2025 Conference Paper

AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification

Weigang Lu
Ziyu Guan
Wei Zhao
Yaming Yang
Yibing Zhan
Yiheng Lu
Dapeng Tao

Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio lambda in the image domain. Recently, the concept of mixup has been adapted to the graph domain through node-centric interpolations. However, these approaches often fail to address the complexity of interconnected relationships, potentially damaging the graph's natural topology and undermining node interactions. Furthermore, current graph mixup methods employ a one-size-fits-all strategy with a randomly sampled lambda for all mixup pairs, ignoring the diverse needs of different pairs. This paper proposes an Adaptive Graph Mixup (AGMixup) framework for semi-supervised node classification. AGMixup introduces a subgraph-centric approach, which treats each subgraph similarly to how images are handled in Euclidean domains, thus facilitating a more natural integration of mixup into graph-based learning. We also propose an adaptive mechanism to tune the mixing ratio lambda for diverse mixup pairs, guided by the contextual similarity and uncertainty of the involved subgraphs. Extensive experiments across seven datasets on semi-supervised node classification benchmarks demonstrate AGMixup's superiority over state-of-the-art graph mixup methods.

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JBHI Journal 2025 Journal Article

CPGNet: Multimodal Graph Learning with Hierarchical Category Guidance for Multi-Label Whole Slide Image Classification

Haoyun Zhao
Dapeng Tao
Yibing Zhan
Jun Ni
Yang Chen

The analysis of WSI categories in digital pathology is critical for clinician decision making regarding the diagnosis, treatment, and prognosis of cancer patients. However, current automated methods for cancer type identification are predominantly formulated as single-label classification problems. These methods typically rely on datasets with relatively balanced and abundant samples, where each WSI belongs to a single category. This approach does not fully align with real-world clinical scenarios, where cancer subtypes often exhibit multi-label characteristics and class imbalance, posing significant challenges. To address this issue, this paper proposes CPGNet, a category-prompted graph network designed as a multi-label WSI classifier better suited for clinical applications. CPGNet employs the MaskSLIC algorithm for superpixel segmentation of WSIs, effectively capturing the nonlinear spatial distribution of cellular and tissue structures. The segmented superpixels are then encoded as graph nodes with their corresponding features, while edges and edge features are constructed to abstractly model WSIs as graphs. Furthermore, the method introduces a GLGFI module, which aggregates features from neighboring nodes and edges via a GNN to capture local information, while simultaneously leveraging a multi-head self-attention mechanism to model global dependencies, mimicking the diagnostic behavior of pathologists. Additionally, a VCI module exploits semantic relationships between categories to guide visual feature classification, providing supplementary cues for accurate predictions. To enhance the model's focus on hard-to-classify positive samples, we also implement a reweighting strategy. The proposed approach is evaluated on a private dataset (YNLUAD) and two public challenge datasets (BCNB and AGGC22). The experimental results demonstrate the superiority, universality, and robustness of CPGNet. The code is available at https://github.com/zhy1312/CPGNet.

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AIJ Journal 2025 Journal Article

NT-FAN: A simple yet effective noise-tolerant few-shot adaptation network

Wenjing Yang
Haoang Chi
Yibing Zhan
Bowen Hu
Xiaoguang Ren
Dapeng Tao
Long Lan

Few-shot domain adaptation (FDA) aims to train a target model with clean labeled data from the source domain and few labeled data from the target domain. Given a limited annotation budget, source data may contain many noisy labels, which can detrimentally impact the performance of models in real-world applications. This problem setting is denoted as wildly few-shot domain adaptation (WFDA), simultaneously taking care of label noise and data shortage. While previous studies have achieved some success, they typically rely on multiple adaptation models to collaboratively filter noisy labels, resulting in substantial computational overhead. To address WFDA more simply and elegantly, we offer a theoretical analysis of this problem and propose a comprehensive upper bound for the excess risk on the target domain. Our theoretical result reveals that correct domain-invariant representations can be obtained even in the presence of source noise and limited target data without incurring additional costs. In response, we propose a simple yet effective WFDA method, referred to as noise-tolerant few-shot adaptation network (NT-FAN). Experiments demonstrate that our method significantly outperforms all the state-of-the-art competitors while maintaining a more lightweight architecture. Notably, NT-FAN consistently exhibits robust performance when dealing with more realistic and intractable source noise (e. g. , instance-dependent label noise) and severe source noise (e. g. , a 40% noise rate) in the source domain.

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EAAI Journal 2024 Journal Article

ASFusion: Adaptive visual enhancement and structural patch decomposition for infrared and visible image fusion

Yiqiao Zhou
Kangjian He
Dan Xu
Dapeng Tao
Xu Lin
Chengzhou Li

Multimodal data fusion plays an increasingly important role in the field of artificial intelligence. The objective of Infrared and Visible Image Fusion (IVF) is to integrate information from different types of images to enhance the performance of target detection tasks. Meanwhile, object detection technology constitutes a crucial foundation in the field of autonomous driving. However, visible images captured under low illumination often lack important details, resulting in suboptimal fusion results，which in turn affects the accuracy of target detection tasks. We proposed an infrared and visible image fusion method based on adaptive visual enhancement and structural patch decomposition (ASFusion) to address the above issues. First, we design an efficient algorithm based on the camera response model to enhance different exposure matrices, allowing for adaptive enhancement of visible images. Second, we decompose the source infrared and the enhanced visible image into three components: mean intensity, signal structure, and signal intensity using structural patch decomposition (SPD), and then design a new degree of membership curve function to estimate the weight of the average intensity component accurately. The estimation process reduces artifacts and preserves the significance of infrared targets. Third, to achieve a higher contrast in the fusion result, we introduced sharpening operations to enhance the detail layer of both the infrared and visible images. Finally, the fused image is obtained by merging the base and detail layers. Through qualitative and quantitative experimental evaluations, the proposed method outperforms twelve state-of-the-art image fusion methods. Additionally, object detection experiments have demonstrated that our ASFusion exhibits tremendous potential in better serving advanced computer vision tasks. Our code is publicly available at https: //github. com/ZhouVMC/ASFusion.

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IJCAI Conference 2024 Conference Paper

MuEP: A Multimodal Benchmark for Embodied Planning with Foundation Models

Kanxue Li
Baosheng Yu
Qi Zheng
Yibing Zhan
Yuhui Zhang
Tianle Zhang
Yijun Yang
Yue Chen

Foundation models have demonstrated significant emergent abilities, holding great promise for enhancing embodied agents' reasoning and planning capacities. However, the absence of a comprehensive benchmark for evaluating embodied agents with multimodal observations in complex environments remains a notable gap. In this paper, we present MuEP, a comprehensive Multimodal benchmark for Embodied Planning. MuEP facilitates the evaluation of multimodal and multi-turn interactions of embodied agents in complex scenes, incorporating fine-grained evaluation metrics that provide insights into the performance of embodied agents throughout each task. Furthermore, we evaluate embodied agents with recent state-of-the-art foundation models, including large language models (LLMs) and large multimodal models (LMMs), on the proposed benchmark. Experimental results show that foundation models based on textual representations of environments usually outperform their visual counterparts, suggesting a gap in embodied planning abilities with multimodal observations. We also find that control language generation is an indispensable ability beyond common-sense knowledge for accurate embodied task completion. We hope the proposed MuEP benchmark can contribute to the advancement of embodied AI with foundation models.

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IJCAI Conference 2024 Conference Paper

Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders

Chuang Liu
Yuyao Wang
Yibing Zhan
Xueqi Ma
Dapeng Tao
Jia Wu
Wenbin Hu

Graph masked autoencoders (GMAE) have emerged as a significant advancement in self-supervised pre-training for graph-structured data. Previous GMAE models primarily utilize a straightforward random masking strategy for nodes or edges during training. However, this strategy fails to consider the varying significance of different nodes within the graph structure. In this paper, we investigate the potential of leveraging the graph's structural composition as a fundamental and unique prior in the masked pre-training process. To this end, we introduce a novel structure-guided masking strategy (i. e. , StructMAE), designed to refine the existing GMAE models. StructMAE involves two steps: 1) Structure-based Scoring: Each node is evaluated and assigned a score reflecting its structural significance. Two distinct types of scoring manners are proposed: predefined and learnable scoring. 2) Structure-guided Masking: With the obtained assessment scores, we develop an easy-to-hard masking strategy that gradually increases the structural awareness of the self-supervised reconstruction task. Specifically, the strategy begins with random masking and progresses to masking structure-informative nodes based on the assessment scores. This design gradually and effectively guides the model in learning graph structural information. Furthermore, extensive experiments consistently demonstrate that our StructMAE method outperforms existing state-of-the-art GMAE models in both unsupervised and transfer learning tasks. Codes are available at https: //github. com/LiuChuang0059/StructMAE.

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IJCAI Conference 2023 Conference Paper

Gapformer: Graph Transformer with Graph Pooling for Node Classification

Chuang Liu
Yibing Zhan
Xueqi Ma
Liang Ding
Dapeng Tao
Jia Wu
Wenbin Hu

Graph Transformers (GTs) have proved their advantage in graph-level tasks. However, existing GTs still perform unsatisfactorily on the node classification task due to 1) the overwhelming unrelated information obtained from a vast number of irrelevant distant nodes and 2) the quadratic complexity regarding the number of nodes via the fully connected attention mechanism. In this paper, we present Gapformer, a method for node classification that deeply incorporates Graph Transformer with Graph Pooling. More specifically, Gapformer coarsens the large-scale nodes of a graph into a smaller number of pooling nodes via local or global graph pooling methods, and then computes the attention solely with the pooling nodes rather than all other nodes. In such a manner, the negative influence of the overwhelming unrelated nodes is mitigated while maintaining the long-range information, and the quadratic complexity is reduced to linear complexity with respect to the fixed number of pooling nodes. Extensive experiments on 13 node classification datasets, including homophilic and heterophilic graph datasets, demonstrate the competitive performance of Gapformer over existing Graph Neural Networks and GTs.

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IJCAI Conference 2019 Conference Paper

Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning

Sihui Luo
Xinchao Wang
Gongfan Fang
Yao Hu
Dapeng Tao
Mingli Song

An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without accessing the training annotations. However, due to the large number of network variants, such public-available trained models are often of different architectures, each of which being tailored for a specific task or dataset. In this paper, we study a deep-model reusing task, where we are given as input pre-trained networks of heterogeneous architectures specializing in distinct tasks, as teacher models. We aim to learn a multitalented and light-weight student model that is able to grasp the integrated knowledge from all such heterogeneous-structure teachers, again without accessing any human annotation. To this end, we propose a common feature learning scheme, in which the features of all teachers are transformed into a common space and the student is enforced to imitate them all so as to amalgamate the intact knowledge. We test the proposed approach on a list of benchmarks and demonstrate that the learned student is able to achieve very promising performance, superior to those of the teachers in their specialized tasks.

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IJCAI Conference 2019 Conference Paper

Pseudo Supervised Matrix Factorization in Discriminative Subspace

Jiaqi Ma
Yipeng Zhang
Lefei Zhang
Bo Du
Dapeng Tao

Non-negative Matrix Factorization (NMF) and spectral clustering have been proved to be efficient and effective for data clustering tasks and have been applied to various real-world scenes. However, there are still some drawbacks in traditional methods: (1) most existing algorithms only consider high-dimensional data directly while neglect the intrinsic data structure in the low-dimensional subspace; (2) the pseudo-information got in the optimization process is not relevant to most spectral clustering and manifold regularization methods. In this paper, a novel unsupervised matrix factorization method, Pseudo Supervised Matrix Factorization (PSMF), is proposed for data clustering. The main contributions are threefold: (1) to cluster in the discriminant subspace, Linear Discriminant Analysis (LDA) combines with NMF to become a unified framework; (2) we propose a pseudo supervised manifold regularization term which utilizes the pseudo-information to instruct the regularization term in order to find subspace that discriminates different classes; (3) an efficient optimization algorithm is designed to solve the proposed problem with proved convergence. Extensive experiments on multiple benchmark datasets illustrate that the proposed model outperforms other state-of-the-art clustering algorithms.

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AAAI Conference 2019 Conference Paper

Semantic Adversarial Network with Multi-Scale Pyramid Attention for Video Classification

De Xie
Cheng Deng
Hao Wang
Chao Li
Dapeng Tao

Two-stream architecture have shown strong performance in video classification task. The key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally. However, there are some problems within such architecture. First, it relies on optical flow to model temporal information, which are often expensive to compute and store. Second, it has limited ability to capture details and local context information for video data. Third, it lacks explicit semantic guidance that greatly decrease the classification performance. In this paper, we proposed a new two-stream based deep framework for video classification to discover spatial and temporal information only from RGB frames, moreover, the multi-scale pyramid attention (MPA) layer and the semantic adversarial learning (SAL) module is introduced and integrated in our framework. The MPA enables the network capturing global and local feature to generate a comprehensive representation for video, and the SAL can make this representation gradually approximate to the real video semantics in an adversarial manner. Experimental results on two public benchmarks demonstrate our proposed methods achieves state-of-the-art results on standard video datasets.

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TIST Journal 2017 Journal Article

Large Sparse Cone Non-negative Matrix Factorization for Image Annotation

Dapeng Tao
Dacheng Tao
Xuelong Li
Xinbo Gao

Image annotation assigns relevant tags to query images based on their semantic contents. Since Non-negative Matrix Factorization (NMF) has the strong ability to learn parts-based representations, recently, a number of algorithms based on NMF have been proposed for image annotation and have achieved good performance. However, most of the efforts have focused on the representations of images and annotations. The properties of the semantic parts have not been well studied. In this article, we revisit the sparseness-constrained NMF (sNMF) proposed by Hoyer [2004]. By endowing the sparseness constraint with a geometric interpretation and sNMF with theoretical analyses of the generalization ability, we show that NMF with such a sparseness constraint has three advantages for image annotation tasks: (i) The sparseness constraint is more ℓ 0 -norm oriented than the ℓ 1 -norm-based sparseness, which significantly enhances the ability of NMF to robustly learn semantic parts. (ii) The sparseness constraint has a large cone interpretation and thus allows the reconstruction error of NMF to be smaller, which means that the learned semantic parts are more powerful to represent images for tagging. (iii) The learned semantic parts are less correlated, which increases the discriminative ability for annotating images. Moreover, we present a new efficient large sparse cone NMF (LsCNMF) algorithm to optimize the sNMF problem by employing the Nesterov’s optimal gradient method. We conducted experiments on the PASCAL VOC07 dataset and demonstrated the effectiveness of LsCNMF for image annotation.

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