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Xun Gong

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6 papers

1 author row

AIIM Journal 2026 Journal Article

Development and validation of deep continual learning model to sequentially learn multiple clinical prediction tasks for ICU patients

Zhixuan Zeng
Yang Liu
Shuo Yao
Xu Cai
Wenbin Nan
Yiyang Xie
Xun Gong

Background ICU patients often suffer from critical and complex condition, and multiple potential risks should be monitored to provide them comprehensive care. However, no study proposes continual learning (CL) model that can effectively solve multiple clinical prediction tasks without catastrophic forgetting. This study proposes three deep CL models for ICU patients. Methods Three public ICU databases were employed. The included patients from MIMIC-III and MIMIC-IV were divided into eight task sets, and the patients from eICU-CRD composed the test set. We propose three CL models (CL_1, CL_2, CL_3) to sequentially learn eight prediction tasks on the eight task sets, and then externally validate them on the test set. We compare our models to three representative baseline CL models and the single-task (ST) and multi-task (MT) model. We train all the CL models under different orders, and evaluate their prediction performance by multiple metrics and their memory ability by backward transfer (BWT). We also analyzed the effect of previously learned tasks on learning new tasks. Results Our three CL models had comparable or slightly weaker performance compared to ST and MT model on the eight tasks. They effectively mitigated catastrophic forgetting, and their performance is robust to different training orders. CL_2 and CL_3 even have improved performance on the current task after learning some previous tasks. Our three CL models outperformed the baseline CL models in most experiments. Conclusions Our CL models are promising to sequentially learn multiple clinical prediction tasks for ICU patients. The CL_2 and CL_3 show the ability of utilizing information of previous tasks to improve learning new tasks. More new datasets and tasks are still needed to further verify the validity of the CL models.

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IJCAI Conference 2025 Conference Paper

Efficient Visual Representation Learning with Heat Conduction Equation

Zhemin Zhang
Xun Gong

Foundation models, such as CNNs and ViTs, have powered the development of image representation learning. However, general guidance to model architecture design is still missing. Inspired by the connection between image representation learning and heat conduction, we model images by the heat conduction equation, where the essential idea is to conceptualize image features as temperatures and model their information interaction as the diffusion of thermal energy. Based on this idea, we find that many modern model architectures, such as residual structures, SE block, and feed-forward networks, can be interpreted from the perspective of the heat conduction equation. Therefore, we leverage the heat equation to design new and more interpretable models. As an example, we propose the Heat Conduction Layer and the Refinement Approximation Layer inspired by solving the heat conduction equation using Finite Difference Method and Fourier series, respectively. The main goal of this paper is to integrate the overall architectural design of neural networks into the theoretical framework of heat conduction. Nevertheless, our Heat Conduction Network (HcNet) still shows competitive performance, e. g. , HcNet-T achieves 83. 0% top-1 accuracy on ImageNet-1K while only requiring 28M parameters and 4. 1G MACs. The code is publicly available at: https: //github. com/ZheminZhang1/HcNet.

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NeurIPS Conference 2025 Conference Paper

Gate to the Vessel: Residual Experts Restore What SAM Overlooks

Weili Jiang
Jinrong Lv
Xun Gong
Xiaomeng Li
Chubin Ou

Foundation segmentation models like Segment Anything (SAM) exhibit strong generalization on natural images but struggle with localized failures in medical imaging, especially on fine-grained structures such as vessels with complex morphology and indistinct boundaries. To address this, we propose FineSAM++, a structure-aware sparse expert framework designed to refine SAM outputs by introducing a confidence-driven soft Routing Module. This module dynamically identifies structurally uncertain regions and activates a lightweight Residual Expert to model and correct residual structural errors only within these areas, thereby achieving efficient "refinement over retraining. " Extensive experiments on five public vascular segmentation datasets demonstrate that FineSAM++ consistently outperforms both SAM-adapted baselines and task-specific models in terms of accuracy, topological consistency. Our results highlight the effectiveness of sparse, structure-driven Mixture-of-Experts (MoE) strategies for enhancing the reliability of foundation vision models in clinical image understanding tasks.

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AAAI Conference 2025 Conference Paper

RepFace: Refining Closed-Set Noise with Progressive Label Correction for Face Recognition

Jie Zhang
Xun Gong
Zhonglin Sun

Face recognition has made remarkable strides, driven by the expanding scale of datasets, advancements in various backbone and discriminative losses. However, face recognition performance is heavily affected by the label noise, especially closed-set noise. While numerous studies have focused on handling label noise, addressing closed-set noise still poses challenges. This paper identifies this challenge as training isn't robust to noise at the early-stage training, and necessitating an appropriate learning strategy for samples with low confidence, which are often misclassified as closed-set noise in later training phases. To address these issues, we propose a new framework to stabilize the training at early stages and split the samples into clean, ambiguous and noisy groups which are devised with separate training strategies. Initially, we employ generated auxiliary closed-set noisy samples to enable the model to identify noisy data at the early stages of training. Subsequently, we introduce how samples are split into clean, ambiguous and noisy groups by their similarity to the positive and nearest negative centers. Then we perform label fusion for ambiguous samples by incorporating accumulated model predictions. Finally, we apply label smoothing within the closed set, adjusting the label to a point between the nearest negative class and the initially assigned label. Extensive experiments validate the effectiveness of our method on mainstream face datasets, achieving state-of-the-art results.

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NeurIPS Conference 2025 Conference Paper

VA-GS: Enhancing the Geometric Representation of Gaussian Splatting via View Alignment

Qing Li
Huifang Feng
Xun Gong
Yu-Shen Liu

3D Gaussian Splatting has recently emerged as an efficient solution for high-quality and real-time novel view synthesis. However, its capability for accurate surface reconstruction remains underexplored. Due to the discrete and unstructured nature of Gaussians, supervision based solely on image rendering loss often leads to inaccurate geometry and inconsistent multi-view alignment. In this work, we propose a novel method that enhances the geometric representation of 3D Gaussians through view alignment (VA). Specifically, we incorporate edge-aware image cues into the rendering loss to improve surface boundary delineation. To enforce geometric consistency across views, we introduce a visibility-aware photometric alignment loss that models occlusions and encourages accurate spatial relationships among Gaussians. To further mitigate ambiguities caused by lighting variations, we incorporate normal-based constraints to refine the spatial orientation of Gaussians and improve local surface estimation. Additionally, we leverage deep image feature embeddings to enforce cross-view consistency, enhancing the robustness of the learned geometry under varying viewpoints and illumination. Extensive experiments on standard benchmarks demonstrate that our method achieves state-of-the-art performance in both surface reconstruction and novel view synthesis. The source code is available at https: //github. com/LeoQLi/VA-GS.

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AAAI Conference 2023 Conference Paper

Positional Label for Self-Supervised Vision Transformer

Zhemin Zhang
Xun Gong

Positional encoding is important for vision transformer (ViT) to capture the spatial structure of the input image. General effectiveness has been proven in ViT. In our work we propose to train ViT to recognize the positional label of patches of the input image, this apparently simple task actually yields a meaningful self-supervisory task. Based on previous work on ViT positional encoding, we propose two positional labels dedicated to 2D images including absolute position and relative position. Our positional labels can be easily plugged into various current ViT variants. It can work in two ways: (a) As an auxiliary training target for vanilla ViT for better performance. (b) Combine the self-supervised ViT to provide a more powerful self-supervised signal for semantic feature learning. Experiments demonstrate that with the proposed self-supervised methods, ViT-B and Swin-B gain improvements of 1.20% (top-1 Acc) and 0.74% (top-1 Acc) on ImageNet, respectively, and 6.15% and 1.14% improvement on Mini-ImageNet. The code is publicly available at: https://github.com/zhangzhemin/PositionalLabel.

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