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Jihua Zhu

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14 papers
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14

AAAI Conference 2026 Conference Paper

Point-SRA: Self-Representation Alignment for 3D Representation Learning

  • Lintong Wei
  • Jian Lu
  • Haozhe Cheng
  • Jihua Zhu
  • Kaibing Zhang

Masked autoencoders (MAE) have become a dominant paradigm in 3D representation learning, setting new performance benchmarks across various downstream tasks. Existing methods with fixed mask ratios neglect multi-level representational correlations and intrinsic geometric structures, while relying on point-wise reconstruction assumptions that conflict with the diversity of point cloud. To address these issues, we propose a 3D representation learning method, termed Point-SRA, which aligns representations through self-distillation and probabilistic modeling. Specifically, we assign different masking ratios to the MAE to capture complementary geometric and semantic information, while the MeanFlow Transformer (MFT) leverages cross-modal conditional embeddings to enable diverse probabilistic reconstruction. Our analysis further reveals that representations at different time steps in MFT also exhibit complementarity. Therefore, a Dual Self-Representation Alignment mechanism is proposed at both the MAE and MFT levels. Finally, we design a Flow-Conditioned Fine-Tuning Architecture to fully exploit the point cloud distribution learned via MeanFlow. Point-SRA outperforms Point-MAE by 5.37% on ScanObjectNN. On intracranial aneurysm segmentation, it reaches 96.07% mean IoU for arteries and 86.87% for aneurysms. For 3D object detection, Point-SRA achieves 47.3% AP@50, surpassing MaskPoint by 5.12%.

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AAAI Conference 2026 Conference Paper

Pseudo Multi-view K-means Clustering

  • Jinqian Chen
  • Jihua Zhu
  • Haoyu Tang
  • Qinghai Zheng

Clustering with k-means is well-established and efficient, but often struggles with complex data distributions because the clustering performance hinges on how well the centroids capture the data distribution, and conventional k-means usually fails to produce representative centroids under such conditions. To address this limitation, we propose Pseudo Multi-view K-means Clustering (PMKC), a novel framework that simulates a multi-view learning paradigm within a single-view setting by generating multiple soft k-means decompositions. Each decomposition can be treated as an individual view and investigates a distinct perspective of the data. Specifically, to encourage complementary structure, we impose an independence constraint among cluster centers, and to integrate these diverse clusterings, we model the soft assignment matrices as a third-order tensor and apply low-rank regularization to extract a shared latent structure. This design not only enhances clustering robustness but also improves the stability and consistency of the final results. Experimental results on several benchmark datasets demonstrate that PMKC achieves superior clustering performance compared to state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

Boundary-Aware Temporal Dynamic Pseudo-Supervision Pairs Generation for Zero-Shot Natural Language Video Localization

  • Xiongwen Deng
  • Haoyu Tang
  • Han Jiang
  • Qinghai Zheng
  • Jihua Zhu

Zero-shot Natural Language Video Localization (NLVL) aims to automatically generate moments and corresponding pseudo queries from raw videos for the training of the localization model without any manual annotations. Existing approaches typically produce pseudo queries as simple words, which overlook the complexity of queries in real-world scenarios. Considering the powerful text modeling capabilities of large language models (LLMs), leveraging LLMs to generate complete queries that are closer to human descriptions is a potential solution. However, directly integrating LLMs into existing approaches introduces several issues, including insensitivity, isolation, and lack of regulation, which prevent the full exploitation of LLMs to enhance zero-shot NLVL performance. To address these issues, we propose BTDP, an innovative framework for Boundary-aware Temporal Dynamic Pseudo-supervision pairs generation. Our method contains two crucial operations: 1) Boundary Segmentation that identifies both visual boundaries and semantic boundaries to generate the atomic segments and activity descriptions, tackling the issue of insensitivity. 2) Context Aggregation that employs the LLMs with a self-evaluation process to aggregate and summarize global video information for optimized pseudo moment-query pairs, tackling the issue of isolation and lack of regulation. Comprehensive experimental results on the Charades-STA and ActivityNet Captions datasets demonstrate the effectiveness of our BTDP method.

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ICRA Conference 2025 Conference Paper

Multiple Rotation Averaging with Constrained Reweighting Deep Matrix Factorization

  • Shiqi Li
  • Jihua Zhu
  • Yifan Xie
  • Naiwen Hu
  • Mingchen Zhu
  • Zhongyu Li 0002
  • Di Wang 0006
  • Huimin Lu 0001

Multiple rotation averaging plays a crucial role in computer vision and robotics domains. The conventional optimization-based methods optimize a nonlinear cost function based on certain noise assumptions, while most previous learning-based methods require ground truth labels in the supervised training process. Recognizing the handcrafted noise assumption may not be reasonable in all real-world scenarios, this paper proposes an effective rotation averaging method for mining data patterns in a learning manner while avoiding the requirement of labels. Specifically, we apply deep matrix factorization to directly solve the multiple rotation averaging problem in free linear space. For deep matrix factorization, we design a neural network model, which is explicitly low-rank and symmetric to better suit the background of multiple rotation averaging. Meanwhile, we utilize a spanning tree-based edge filtering to suppress the influence of rotation outliers. What's more, we also adopt a reweighting scheme and dynamic depth selection strategy to further improve the robustness. Our method synthesizes the merit of both optimization-based and learning-based methods. Experimental results on various datasets validate the effectiveness of our proposed method.

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IROS Conference 2024 Conference Paper

Hyperbolic Image-and-Pointcloud Contrastive Learning for 3D Classification

  • Naiwen Hu
  • Haozhe Cheng
  • Yifan Xie
  • Pengcheng Shi
  • Jihua Zhu

3D contrastive representation learning has exhibited remarkable efficacy across various downstream tasks. However, existing contrastive learning paradigms based on cosine similarity fail to deeply explore the potential intra-modal hierarchical and cross-modal semantic correlations about multi-modal data in Euclidean space. In response, we seek solutions in hyperbolic space and propose a hyperbolic image-and-pointcloud contrastive learning method (HyperIPC). For the intra-modal branch, we rely on the intrinsic geometric structure to explore the hyperbolic embedding representation of point cloud to capture invariant features. For the cross-modal branch, we leverage images to guide the point cloud in establishing strong semantic hierarchical correlations. Empirical experiments underscore the outstanding classification performance of HyperIPC. Notably, HyperIPC enhances object classification results by 2. 8% and few-shot classification outcomes by 5. 9% on ScanObjectNN compared to the baseline. Furthermore, ablation studies and confirmatory testing validate the rationality of HyperIPC’s parameter settings and the effectiveness of its submodules.

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AAAI Conference 2024 Conference Paper

Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models

  • Jinqian Chen
  • Jihua Zhu
  • Qinghai Zheng
  • Zhongyu Li
  • Zhiqiang Tian

Federated learning encounters substantial challenges with heterogeneous data, leading to performance degradation and convergence issues. While considerable progress has been achieved in mitigating such an impact, the reliability aspect of federated models has been largely disregarded. In this study, we conduct extensive experiments to investigate the reliability of both generic and personalized federated models. Our exploration uncovers a significant finding: federated models exhibit unreliability when faced with heterogeneous data, demonstrating poor calibration on in-distribution test data and low uncertainty levels on out-of-distribution data. This unreliability is primarily attributed to the presence of biased projection heads, which introduce miscalibration into the federated models. Inspired by this observation, we propose the "Assembled Projection Heads" (APH) method for enhancing the reliability of federated models. By treating the existing projection head parameters as priors, APH randomly samples multiple initialized parameters of projection heads from the prior and further performs targeted fine-tuning on locally available data under varying learning rates. Such a head ensemble introduces parameter diversity into the deterministic model, eliminating the bias and producing reliable predictions via head averaging. We evaluate the effectiveness of the proposed APH method across three prominent federated benchmarks. Experimental results validate the efficacy of APH in model calibration and uncertainty estimation. Notably, APH can be seamlessly integrated into various federated approaches but only requires less than 30% additional computation cost for 100x inferences within large models.

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AAAI Conference 2023 Conference Paper

3D-TOGO: Towards Text-Guided Cross-Category 3D Object Generation

  • Zutao Jiang
  • Guansong Lu
  • Xiaodan Liang
  • Jihua Zhu
  • Wei Zhang
  • Xiaojun Chang
  • Hang Xu

This article has been updated and an error has been fixed in published paper. An Erratum to this article was published on 6 September 2023. Text-guided 3D object generation aims to generate 3D objects described by user-defined captions, which paves a flexible way to visualize what we imagined. Although some works have been devoted to solving this challenging task, these works either utilize some explicit 3D representations (e.g., mesh), which lack texture and require post-processing for rendering photo-realistic views; or require individual time-consuming optimization for every single case. Here, we make the first attempt to achieve generic text-guided cross-category 3D object generation via a new 3D-TOGO model, which integrates a text-to-views generation module and a views-to-3D generation module. The text-to-views generation module is designed to generate different views of the target 3D object given an input caption. prior-guidance, caption-guidance and view contrastive learning are proposed for achieving better view-consistency and caption similarity. Meanwhile, a pixelNeRF model is adopted for the views-to-3D generation module to obtain the implicit 3D neural representation from the previously-generated views. Our 3D-TOGO model generates 3D objects in the form of the neural radiance field with good texture and requires no time-cost optimization for every single caption. Besides, 3D-TOGO can control the category, color and shape of generated 3D objects with the input caption. Extensive experiments on the largest 3D object dataset (i.e., ABO) are conducted to verify that 3D-TOGO can better generate high-quality 3D objects according to the input captions across 98 different categories, in terms of PSNR, SSIM, LPIPS and CLIP-score, compared with text-NeRF and Dreamfields.

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IJCAI Conference 2023 Conference Paper

Contrastive Label Enhancement

  • Yifei Wang
  • Yiyang Zhou
  • Jihua Zhu
  • Xinyuan Liu
  • Wenbiao Yan
  • Zhiqiang Tian

Label distribution learning (LDL) is a new machine learning paradigm for solving label ambiguity. Since it is difficult to directly obtain label distributions, many studies are focusing on how to recover label distributions from logical labels, dubbed label enhancement (LE). Existing LE methods estimate label distributions by simply building a mapping relationship between features and label distributions under the supervision of logical labels. They typically overlook the fact that both features and logical labels are descriptions of the instance from different views. Therefore, we propose a novel method called Contrastive Label Enhancement (ConLE) which integrates features and logical labels into the unified projection space to generate high-level features by contrastive learning strategy. In this approach, features and logical labels belonging to the same sample are pulled closer, while those of different samples are projected farther away from each other in the projection space. Subsequently, we leverage the obtained high-level features to gain label distributions through a well-designed training strategy that considers the consistency of label attributes. Extensive experiments on LDL benchmark datasets demonstrate the effectiveness and superiority of our method.

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AIIM Journal 2022 Journal Article

Automatic pediatric congenital heart disease classification based on heart sound signal

  • Weize Xu
  • Kai Yu
  • Jingjing Ye
  • Haomin Li
  • Jiajia Chen
  • Fei Yin
  • Jingfang Xu
  • Jihua Zhu

Congenital heart diseases (CHD) are the most common birth defects, and the early diagnosis of CHD is crucial for CHD therapy. However, there are relatively few studies on intelligent auscultation for pediatric CHD, due to the fact that effective cooperation of the patient is required for the acquisition of useable heart sounds by electronic stethoscopes, yet the quality of heart sounds in pediatric is poor compared to adults due to the factors such as crying and breath sounds. This paper presents a novel pediatric CHD intelligent auscultation method based on electronic stethoscope. Firstly, a pediatric CHD heart sound database with a total of 941 PCG signal is established. Then a segment-based heart sound segmentation algorithm is proposed, which is based on PCG segment to achieve the segmentation of cardiac cycles, and therefore can reduce the influence of local noise to the global. Finally, the accurate classification of CHD is achieved using a majority voting classifier with Random Forest and Adaboost classifier based on 84 features containing time domain and frequency domain. Experimental results show that the performance of the proposed method is competitive, and the accuracy, sensitivity, specificity and f1-score of classification for CHD are 0. 953, 0. 946, 0. 961 and 0. 953 respectively.

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AAAI Conference 2022 Conference Paper

Coarse-to-Fine Generative Modeling for Graphic Layouts

  • Zhaoyun Jiang
  • Shizhao Sun
  • Jihua Zhu
  • Jian-Guang Lou
  • Dongmei Zhang

Even though graphic layout generation has attracted growing attention recently, it is still challenging to synthesis realistic and diverse layouts, due to the complicated element relationships and varied element arrangements. In this work, we seek to improve the performance of layout generation by incorporating the concept of regions, which consist of a smaller number of elements and appears like a simple layout, into the generation process. Specifically, we leverage Variational Autoencoder (VAE) as the overall architecture and decompose the decoding process into two stages. The first stage predicts representations for regions, and the second stage fills in the detailed position for each element within the region based on the predicted region representation. Compared to prior studies that merely abstract the layout into a list of elements and generate all the element positions in one go, our approach has at least two advantages. First, by the two-stage decoding, our approach decouples the complex layout generation task into several simple layout generation tasks, which reduces the problem difficulty. Second, the predicted regions can help the model roughly know what the graphic layout looks like and serve as global context to improve the generation of detailed element positions. Qualitative and quantitative experiments demonstrate that our approach significantly outperforms the existing methods, especially on the complex graphic layouts.

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ICRA Conference 2021 Conference Paper

Robust Motion Averaging under Maximum Correntropy Criterion

  • Jihua Zhu
  • Jie Hu
  • Huimin Lu 0001
  • Badong Chen
  • Zhongyu Li 0002
  • Yaochen Li

Recently, the motion averaging method has been introduced as an effective means to solve the multi-view registration problem. This method aims to recover global motions from a set of relative motions, where the original method is sensitive to outliers due to using the Frobenius norm error in the optimization. Accordingly, this paper proposes a novel robust motion averaging method based on the maximum correntropy criterion (MCC). Specifically, the correntropy measure is used instead of utilizing Frobenius norm error to improve the robustness of motion averaging against outliers. According to the half-quadratic technique, the correntropy measure based optimization problem can be solved by the alternating minimization procedure, which includes operations of weight assignment and weighted motion averaging. Further, we design a selection strategy of adaptive kernel width to take advantage of correntropy. Experimental results on benchmark data sets illustrate that our method has superior performance on accuracy and robustness for multi-view registration. What’s more, it can be applied to robot mapping.

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AAAI Conference 2020 Conference Paper

Label Enhancement with Sample Correlations via Low-Rank Representation

  • Haoyu Tang
  • Jihua Zhu
  • Qinghai Zheng
  • Jun Wang
  • Shanmin Pang
  • Zhongyu Li

Compared with single-label and multi-label annotations, label distribution describes the instance by multiple labels with different intensities and accommodates to more-general conditions. Nevertheless, label distribution learning is unavailable in many real-world applications because most existing datasets merely provide logical labels. To handle this problem, a novel label enhancement method, Label Enhancement with Sample Correlations via low-rank representation, is proposed in this paper. Unlike most existing methods, a low-rank representation method is employed so as to capture the global relationships of samples and predict implicit label correlation to achieve label enhancement. Extensive experiments on 14 datasets demonstrate that the algorithm accomplishes stateof-the-art results as compared to previous label enhancement baselines.

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IS Journal 2008 Journal Article

50 Years of Image Processing and Pattern Recognition in China

  • Nanning Zheng
  • Qubo You
  • Gaofeng Meng
  • Jihua Zhu
  • Shaoyi Du
  • Jianyi Liu

This article briefly reviews the development of image recognition in and outside China. It presents theoretical research achievements and applied research as well as several typical applications of image recognition in China. Finally, it discusses future trends in image recognition integrated with cognitive science. This article is part of a special issue on AI in China.

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v2026.09.13