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Jinfeng Xu

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6 papers

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AAAI Conference 2026 Conference Paper

Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering

Jinfeng Xu
Zheyu Chen
Shuo Yang
Jinze Li
Ziyue Peng
Zewei Liu
Hewei Wang
Jiayi Zhang

Multiple clustering aims to discover diverse latent structures from different perspectives, yet existing methods generate exhaustive clusterings without discerning user interest, necessitating laborious manual screening. Current multi-modal solutions suffer from static semantic rigidity: predefined candidate words fail to adapt to dataset-specific concepts, and fixed fusion strategies ignore evolving feature interactions. To overcome these limitations, we propose Multi-DProxy, a novel multi-modal dynamic proxy learning framework that leverages cross-modal alignment through learnable textual proxies. Multi-DProxy introduces 1) gated cross-modal fusion that synthesizes discriminative joint representations by adaptively modeling feature interactions. 2) dual-constraint proxy optimization where user interest constraints enforce semantic consistency with domain concepts while concept constraints employ hard example mining to enhance cluster discrimination. 3) dynamic candidate management that refines textual proxies through iterative clustering feedback. Therefore, Multi-DProxy not only effectively captures a user's interest through proxies but also enables the identification of relevant clusterings with greater precision. Extensive experiments demonstrate state-of-the-art performance with significant improvements over existing methods across a broad set of multi-clustering benchmarks.

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AAAI Conference 2025 Conference Paper

MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation

Jinfeng Xu
Zheyu Chen
Shuo Yang
Jinze Li
Hewei Wang
Edith C. H. Ngai

As multimedia information proliferates, multimodal recommendation systems have garnered significant attention. These systems leverage multimodal information to alleviate the data sparsity issue inherent in recommendation systems, thereby enhancing the accuracy of recommendations. Due to the natural semantic disparities among multimodal features, recent research has primarily focused on cross-modal alignment using self-supervised learning to bridge these gaps. However, aligning different modal features might result in the loss of valuable interaction information, distancing them from ID embeddings. It is crucial to recognize that the primary goal of multimodal recommendation is to predict user preferences, not merely to understand multimodal content. To this end, we propose a new Multi-level sElf-supervised learNing for mulTimOdal Recommendation (MENTOR) method, which effectively reduces the gap among modalities while retaining interaction information. Specifically, MENTOR begins by extracting representations from each modality using both heterogeneous user-item and homogeneous item-item graphs. It then employs a multilevel cross-modal alignment task, guided by ID embeddings, to align modalities across multiple levels while retaining historical interaction information. To balance effectiveness and efficiency, we further propose an optional general feature enhancement task that bolsters the general features from both structure and feature perspectives, thus enhancing the robustness of our model.

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AAAI Conference 2025 Conference Paper

More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding

Yuan Tang
Xu Han
Xianzhi Li
Qiao Yu
Jinfeng Xu
Yixue Hao
Long Hu
Min Chen

Enabling Large Language Models (LLMs) to comprehend the 3D physical world remains a significant challenge. Due to the lack of large-scale 3D-text pair datasets, the success of LLMs has yet to be replicated in 3D understanding. In this paper, we rethink this issue and propose a new task: 3D Data-Efficient Point-Language Understanding. The goal is to enable LLMs to achieve robust 3D object understanding with minimal 3D point cloud and text data pairs. To address this task, we introduce GreenPLM, which leverages more text data to compensate for the lack of 3D data. First, inspired by using CLIP to align images and text, we utilize a pre-trained point cloud-text encoder to map the 3D point cloud space to the text space. This mapping leaves us to seamlessly connect the text space with LLMs. Once the point-text-LLM connection is established, we further enhance text-LLM alignment by expanding the intermediate text space, thereby reducing the reliance on 3D point cloud data. Specifically, we generate 6M free-text descriptions of 3D objects, and design a three-stage training strategy to help LLMs better explore the intrinsic connections between different modalities. To achieve efficient modality alignment, we design a zero-parameter cross-attention module for token pooling. Extensive experimental results show that GreenPLM requires only 12% of the 3D training data used by existing state-of-the-art models to achieve superior 3D understanding. Remarkably, GreenPLM also achieves competitive performance using text-only data.

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JBHI Journal 2024 Journal Article

A ROI Extraction Method for Wrist Imaging Applied in Smart Bone-Age Assessment System

Lina Wang
Yan Mao
Jinfeng Xu
Jianan Wu
Kunxiu Wu
Keji Mao
Kai Fang

Bone Age (BA) is reckoned to be closely associated with the growth and development of teenagers, whose assessment highly depends on the accurate extraction of the reference bone from the carpal bone. Being uncertain in its proportion and irregular in its shape, wrong judgment and poor average extraction accuracy of the reference bone will no doubt lower the accuracy of Bone Age Assessment (BAA). In recent years, machine learning and data mining are widely embraced in smart healthcare systems. Using these two instruments, this article aims to tackle the aforementioned problems by proposing a Region of Interest (ROI) extraction method for wrist X-ray images based on optimized YOLO model. The method combines Deformable convolution-focus (Dc-focus), Coordinate attention (Ca) module, Feature level expansion, and Efficient Intersection over Union (EIoU) loss all together as YOLO-DCFE. With the improvement, the model can better extract the features of irregular reference bone and reduce the potential misdiscrimination between the reference bone and other similarly shaped reference bones, improving the detection accuracy. We select 10041 images taken by professional medical cameras as the dataset to test the performance of YOLO-DCFE. Statistics show the advantages of YOLO-DCFE in detection speed and high accuracy. The detection accuracy of all ROIs is 99. 8%, which is higher than other models. Meanwhile, YOLO-DCFE is the fastest of all comparison models, with the Frames Per Second (FPS) reaching 16.

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AAAI Conference 2023 Conference Paper

CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusion

Jinfeng Xu
Xianzhi Li
Yuan Tang
Qiao Yu
Yixue Hao
Long Hu
Min Chen

Semantic scene completion (SSC) aims to complete a partial 3D scene and predict its semantics simultaneously. Most existing works adopt the voxel representations, thus suffering from the growth of memory and computation cost as the voxel resolution increases. Though a few works attempt to solve SSC from the perspective of 3D point clouds, they have not fully exploited the correlation and complementarity between the two tasks of scene completion and semantic segmentation. In our work, we present CasFusionNet, a novel cascaded network for point cloud semantic scene completion by dense feature fusion. Specifically, we design (i) a global completion module (GCM) to produce an upsampled and completed but coarse point set, (ii) a semantic segmentation module (SSM) to predict the per-point semantic labels of the completed points generated by GCM, and (iii) a local refinement module (LRM) to further refine the coarse completed points and the associated labels from a local perspective. We organize the above three modules via dense feature fusion in each level, and cascade a total of four levels, where we also employ feature fusion between each level for sufficient information usage. Both quantitative and qualitative results on our compiled two point-based datasets validate the effectiveness and superiority of our CasFusionNet compared to state-of-the-art methods in terms of both scene completion and semantic segmentation. The codes and datasets are available at: https://github.com/JinfengX/CasFusionNet.

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JMLR Journal 2018 Journal Article

Online Bootstrap Confidence Intervals for the Stochastic Gradient Descent Estimator

Yixin Fang
Jinfeng Xu
Lei Yang

In many applications involving large dataset or online learning, stochastic gradient descent (SGD) is a scalable algorithm to compute parameter estimates and has gained increasing popularity due to its numerical convenience and memory efficiency. While the asymptotic properties of SGD-based estimators have been well established, statistical inference such as interval estimation remains much unexplored. The classical bootstrap is not directly applicable if the data are not stored in memory. The plug-in method is not applicable when there is no explicit formula for the covariance matrix of the estimator. In this paper, we propose an online bootstrap procedure for the estimation of confidence intervals, which, upon the arrival of each observation, updates the SGD estimate as well as a number of randomly perturbed SGD estimates. The proposed method is easy to implement in practice. We establish its theoretical properties for a general class of models that includes linear regressions, generalized linear models, M-estimators and quantile regressions as special cases. The finite-sample performance and numerical utility is evaluated by simulation studies and real data applications. [abs] [ pdf ][ bib ] &copy JMLR 2018. ( edit, beta )

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