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Miao Hu

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8 papers

1 author row

AAAI Conference 2026 Conference Paper

Rethinking Multimodal Point Cloud Completion: A Completion-by-Correction Perspective

Wang Luo
Di Wu
Hengyuan Na
Yinlin Zhu
Miao Hu
Guocong Quan

Point cloud completion aims to reconstruct complete 3D shapes from partial observations, which is a challenging problem due to severe occlusions and missing geometry. Despite recent advances in multimodal techniques that leverage complementary RGB images to compensate for missing geometry, most methods still follow a Completion-by-Inpainting paradigm, synthesizing missing structures from fused latent features. We empirically show that this paradigm often results in structural inconsistencies and topological artifacts due to limited geometric and semantic constraints. To address this, we rethink the task and propose a more robust paradigm, termed Completion-by-Correction, which begins with a topologically complete shape prior generated by a pretrained image-to-3D model and performs feature-space correction to align it with the partial observation. This paradigm shifts completion from unconstrained synthesis to guided refinement, enabling structurally consistent and observation-aligned reconstruction. Building upon this paradigm, we introduce PGNet, a multi-stage framework that conducts dual-feature encoding to ground the generative prior, synthesizes a coarse yet structurally aligned scaffold, and progressively refines geometric details via hierarchical correction. Experiments on the ShapeNetViPC dataset demonstrate the superiority of PGNet over state-of-the-art baselines in terms of average Chamfer Distance (-23.5%) and F-score (+7.1%).

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JBHI Journal 2026 Journal Article

SSDiff: A Contrast-Free Virtual LGE Generator for Acute Myocardial Infarction with Joint Segmentation via Diffusion Model

Jing Qi
Xiuzheng Yue
Miao Hu
Xin Wen
Yinyin Chen
Hang Jin
Chengyan Wang
Tao Li

Myocardial infarction (MI) remains a major cause of death and disability. Although late gadolinium enhancement (LGE) cardiac MRI is the reference for assessing myocardial viability, it requires contrast injection, complex protocols, and added cost. Prior virtual LGE approaches-mostly GAN-based-mainly use cine or T1 mapping and ignore T2-weighted short-tau inversion recovery (T2-STIR), which is highly sensitive to edema in acute MI. They also typically require manual post-hoc delineation of infarcts. We propose SSDiff ( S ynthesis joint S egmentation Diff usion), a multitask conditional diffusion framework that synthesizes contrast-free virtual LGE from routine cine + T2-STIR for acute infarct assessment and simultaneously segments myocardium, ventricular blood pool, and infarct. SSDiff introduces a feature-disentangled attention module that isolates sequence-specific cues to steer the diffusion process, and a cross-fusion module that aligns synthesis and segmentation decoders for mutual optimization. Evaluated on a multi-center, multi-vendor dataset of 409 subjects (2, 177 aligned cine-T2-STIR-LGE triplets), SSDiff yields significant gains in synthetic image quality and downstream segmentation accuracy over strong baselines. Beyond serving as a clinically feasible alternative when LGE is unavailable or contraindicated, SSDiff also generates paired image-mask samples that augment LGE-scarce training, highlighting its practical utility and translational potential. Code is available at: https://github.com/QijingGJ/SSDiff.

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EAAI Journal 2025 Journal Article

SARA: Span-aware framework with relation-augmented grid tagging for conversational aspect-based sentiment quadruple analysis

Xiaoyong Liu
Miao Hu
Chunlin Xu
Zhiguo Du

Conversational aspect-based sentiment quadruple analysis (DiaASQ) is an emerging research topic in the field of aspect-based sentiment analysis (ABSA), which aims to extract sentiment quadruples (i. e. , target, aspect, opinion, and sentiment polarity) from a given dialogue. In DiaASQ, numerous span quadruples can be observed, where the constituent elements of these quadruples are often dispersed across a single utterance or span multiple utterances within a conversation, posing significant challenges to previous methods. In addition, existing methods typically focus on linking the target term with the opinion term, while overlooking the direct relationship between the aspect term and the opinion term, leading to inaccurate extraction of quadruples. Therefore, this paper proposes a Span-Aware framework with Relation-Augmented grid tagging scheme (SARA) to enhance DiaASQ. Specifically, a novel span-aware memory block is devised to enhance the model’s ability to obtain the long-distance dependency among elements within span quadruples. Furthermore, a new relation-augmented grid tagging scheme is introduced to strengthen the pairing of aspect terms and opinion terms, thereby enhancing the performance of quadruple extraction. Extensive experimental results on two benchmark datasets validate the effectiveness of the proposed approach.

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NeurIPS Conference 2025 Conference Paper

Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement

Yinlin Zhu
Xunkai Li
Jishuo Jia
Miao Hu
Di Wu
Meikang Qiu

Recent advances in graph machine learning have shifted to data-centric paradigms, driven by two emerging research fields: (1) Federated graph learning (FGL) facilitates multi-client collaboration but struggles with data and task heterogeneity, resulting in limited practicality; (2) Graph foundation model (GFM) enables desirable domain generalization but is typically confined to single-machine training, neglecting the potential of cross-silo data and computational resources. It is evident that these two paradigms are complementary, and their integration offers substantial advantages. Motivated by this, we present a pioneering study about the federated graph foundation model (FedGFM), a novel decentralized GFM training paradigm. Despite the promising vision of FedGFM, knowledge entanglement has emerged as a critical challenge, where multi-domain knowledge is encoded into indistinguishable representations, thereby limiting downstream adaptation. To this end, we propose FedGFM+, an effective FedGFM framework with two key modules to mitigate knowledge entanglement in a dual-pronged manner. (1) AncDAI: From a global perspective, we introduce a novel anchor-based domain-aware initialization strategy. Before pre-training, each client encodes its local graph into a domain-specific prototypes, which serve as semantic anchors in the representation space. Around each anchor, we construct synthetic embeddings to initialize the global model. We theoretically show that these prototypes are distinguishable across domains, and the initialization provides a strong inductive bias that facilitates disentanglement of domain-specific knowledge. (2) AdaDPP: From a local perspective, during pre-training, each client independently learns a lightweight graph prompt that captures domain semantic preferences. During fine-tuning, prompts from all clients are aggregated into an adaptive domain-sensitive prompt pool, from which the GFM selects relevant prompts to augment the target graph’s attributes, thereby improving the downstream adaptation. FedGFM+ is extensively evaluated on 8 diverse benchmarks spanning multiple domains and tasks, outperforming 20 baselines from isolated supervised learning, FGL, and federated variants of centralized GFM paradigms.

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IJCAI Conference 2024 Conference Paper

FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning

Yinlin Zhu
Xunkai Li
Zhengyu Wu
Di Wu
Miao Hu
Rong-Hua Li

Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfortunately, a significant challenge of subgraph-FL arises from subgraph heterogeneity, which stems from node and topology variation, causing the impaired performance of the global GNN. Despite various studies, they have not yet thoroughly investigated the impact mechanism of subgraph heterogeneity. To this end, we decouple node and topology variation, revealing that they correspond to differences in label distribution and structure homophily. Remarkably, these variations lead to significant differences in the class-wise knowledge reliability of multiple local GNNs, misguiding the model aggregation with varying degrees. Building on this insight, we propose topology-aware data-free knowledge distillation technology (FedTAD), enhancing reliable knowledge transfer from the local model to the global model. Extensive experiments on six public datasets consistently demonstrate the superiority of FedTAD over state-of-the-art baselines.

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EAAI Journal 2023 Journal Article

A systematic empirical study on word embedding based methods in discovering Chinese black keywords

Chenyang Wang
YI Shen
Yuwei Li
Min Zhang
Miao Hu
Jinghua Zheng

With the development of online transactions, the Chinese cyber black market is proliferating and facilitates many cybercrimes. It is difficult to understand the cyber black market due to the confusing jargon (called black keywords in this paper) used by criminals to conceal underground transactions. To discover black keywords automatically, some natural language processing based methods have been proposed by comparing the similarity of word vectors generated by word embedding models. Therefore, the quality of word vectors generated has a significant impact on black keyword discovery and it is necessary to evaluate different word embedding models in discovering black keywords. To this end, we design a Chinese black keyword discovery framework and conduct a systematic empirical study on six existing word embedding models including both static and dynamic types in discovering Chinese black keywords. In specific, we classify Chinese black keywords in four types: domain specific words (DSWs), new meaning words (NMWs), similar pronunciation words (SPWs), and similar glyph words (SGWs). We experimentally find that different word embedding models vary greatly in performance when discovering black keywords, e. g. , dynamic models perform well in discovering DSWs and NMWs, static ones perform poorly in discovering NMWs. We improve the static word embedding model based NMW discovery algorithm by additionally comparing the differences in cross-corpus word nearest-neighbors before and after domain incremental training. For effectively discovering variant words like SPWs and SGWs, we additionally introduce Chinese pronunciation and glyph features. The experimental results demonstrate the effectiveness of the proposed Chinese black keyword discovery framework, with detection accuracies of over 90% for DSWs, 80% for NWMs, 90% for SPWs, and 61% for SGWs.

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IJCAI Conference 2023 Conference Paper

BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning

Yunchao Yang
Yipeng Zhou
Miao Hu
Di Wu
Quan Z. Sheng

Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an incentive mechanism is indispensable for motivating data owners to contribute their models to FL training. However, how to allocate the reward budget among different rounds is an essential but complicated problem largely overlooked by existing works. The challenge of this problem lies in the opaque feedback between reward budget allocation and model utility improvement of FL, making the optimal reward budget allocation complicated. To address this problem, we design an online reward budget allocation algorithm using Bayesian optimization named BARA (Budget Allocation for Reverse Auction). Specifically, BARA can model the complicated relationship between reward budget allocation and final model accuracy in FL based on historical training records so that the reward budget allocated to each communication round is dynamically optimized so as to maximize the final model utility. We further incorporate the BARA algorithm into reverse auction-based incentive mechanisms to illustrate its effectiveness. Extensive experiments are conducted on real datasets to demonstrate that BARA significantly outperforms competitive baselines by improving model utility with the same amount of reward budget.

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IJCAI Conference 2023 Conference Paper

FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment

Jiahao Liu
Jiang Wu
Jinyu Chen
Miao Hu
Yipeng Zhou
Di Wu

Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model parameters among different clients. However, such kinds of methods require clients to download others' models. It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. In this way, FedDWA can capture similarities between clients with much less communication overhead. More specifically, we formulate the PFL problem as an optimization problem by minimizing the distance between personalized models and guidance models, so as to customize aggregation weights for each client. Guidance models are obtained by the local one-step ahead adaptation on individual clients. Finally, we conduct extensive experiments using five real datasets and the results demonstrate that FedDWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches.

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