Author name cluster

Xiaojuan Ban

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

AAAI Conference 2026 Conference Paper

HC2-GNN: Hierarchical Graph Representation Learning for Efficient Text Classification

jiejie fan
Xiaojuan Ban
Zhiyan Zhang
Xi Sun

Graph Neural Networks (GNNs) offer superior modeling capabilities for text classification by capturing complex spatial features within semantic representations. However, existing graph-based approaches often suffer from computational inefficiency and limited ability to model both fine-grained local structures and the sequential nature of text. To address these challenges, we propose HC2-GNN, a Hierarchical Clustering and Coarsening Graph Neural Network, which introduces a novel lightweight graph clustering algorithm called Compromise Conductance Graph Clustering (C2GC). C2GC enables efficient graph clustering while simultaneously preserving both the textual order and the topological coherence of subgraphs. Furthermore, it incorporates a virtue cluster mechanism that expands each subgraph with semantically relevant neighbors, explicitly enabling cross-cluster information propagation without compromising local structural integrity. HC2-GNN aggregates local and global features by combining subgraph-level and full-graph representations, enhancing semantic discriminability for classification. Extensive experiments on benchmark datasets demonstrate that HC2-GNN consistently outperforms existing state-of-the-art text classification methods.

PDF Details DOI

IJCAI Conference 2024 Conference Paper

Enhancing Boundary Segmentation for Topological Accuracy with Skeleton-based Methods

Chuni Liu
Boyuan Ma
Xiaojuan Ban
Yujie Xie
Hao Wang
Weihua Xue
Jingchao Ma
Ke Xu

Topological consistency plays a crucial role in the task of boundary segmentation for reticular images, such as cell membrane segmentation in neuron electron microscopic images, grain boundary segmentation in material microscopic images and road segmentation in aerial images. In these fields, topological changes in segmentation results have a serious impact on the downstream tasks, which can even exceed the misalignment of the boundary itself. To enhance the topology accuracy in segmentation results, we propose the Skea-Topo Aware loss, which is a novel loss function that takes into account the shape of each object and topological significance of the pixels. It consists of two components. First, a skeleton-aware weighted loss improves the segmentation accuracy by better modeling the object geometry with skeletons. Second, a boundary rectified term effectively identifies and emphasizes topological critical pixels in the prediction errors using both foreground and background skeletons in the ground truth and predictions. Experiments prove that our method improves topological consistency by up to 7 points in VI compared to 13 state-of-art methods, based on objective and subjective assessments across three different boundary segmentation datasets. The code is available at https: //github. com/clovermini/Skea_topo.

PDF Details DOI

AAAI Conference 2024 Conference Paper

FedST: Federated Style Transfer Learning for Non-IID Image Segmentation

Boyuan Ma
Xiang Yin
Jing Tan
Yongfeng Chen
Haiyou Huang
Hao Wang
Weihua Xue
Xiaojuan Ban

Federated learning collaboratively trains machine learning models among different clients while keeping data privacy and has become the mainstream for breaking data silos. However, the non-independently and identically distribution (i.e., Non-IID) characteristic of different image domains among different clients reduces the benefits of federated learning and has become a bottleneck problem restricting the accuracy and generalization of federated models. In this work, we propose a novel federated image segmentation method based on style transfer, FedST, by using a denoising diffusion probabilistic model to achieve feature disentanglement and image synthesis of cross-domain image data between multiple clients. Thus it can share style features among clients while protecting structure features of image data, which effectively alleviates the influence of the Non-IID phenomenon. Experiments prove that our method achieves superior segmentation performance compared to state-of-art methods among four different Non-IID datasets in objective and subjective assessment. The code is available at https://github.com/YoferChen/FedST.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Spatial-Related Sensors Matters: 3D Human Motion Reconstruction Assisted with Textual Semantics

Xueyuan Yang
Chao Yao
Xiaojuan Ban

Leveraging wearable devices for motion reconstruction has emerged as an economical and viable technique. Certain methodologies employ sparse Inertial Measurement Units (IMUs) on the human body and harness data-driven strategies to model human poses. However, the reconstruction of motion based solely on sparse IMU data is inherently fraught with ambiguity, a consequence of numerous identical IMU readings corresponding to different poses. In this paper, we explore the spatial importance of sparse sensors, supervised by text that describes specific actions. Specifically, uncertainty is introduced to derive weighted features for each IMU. We also design a Hierarchical Temporal Transformer (HTT) and apply contrastive learning to achieve precise temporal and feature alignment of sensor data with textual semantics. Experimental results demonstrate our proposed approach achieves significant improvements in multiple metrics compared to existing methods. Notably, with textual supervision, our method not only differentiates between ambiguous actions such as sitting and standing but also produces more precise and natural motion.

PDF Details DOI

IJCAI Conference 2024 Conference Paper

Who Looks like Me: Semantic Routed Image Harmonization

Jinsheng Sun
Chao Yao
Xiaokun Wang
Yu Guo
Yalan Zhang
Xiaojuan Ban

Image harmonization, aiming to seamlessly blend extraneous foreground objects with background images, is a promising and challenging task. Ensuring a synthetic image appears realistic requires maintaining consistency in visual characteristics, such as texture and style, across global and semantic regions. In this paper, We approach image harmonization as a semantic routed style transfer problem, and propose an imageharmonization model by routing semantic similarity explicitly to enhance the consistency of appearance characteristics. To refine calculate the similarity between the composed foreground and background instance, we propose an InstanceSimilarity Evaluation Module(ISEM). To harness analogous semantic information effectively, we further introduceStyle Transfer Block(STB) to establish fine-grained foreground-background semantic correlation. Our method has achieved excellent experimental results on existing datasets and our model outperforms the state-of-the-art by a margin of 0. 45 dB on iHarmony4 dataset.

PDF Details DOI

AAAI Conference 2023 Conference Paper

ODE-RSSM: Learning Stochastic Recurrent State Space Model from Irregularly Sampled Data

Zhaolin Yuan
Xiaojuan Ban
Zixuan Zhang
Xiaorui Li
Hong-Ning Dai

For the complicated input-output systems with nonlinearity and stochasticity, Deep State Space Models (SSMs) are effective for identifying systems in the latent state space, which are of great significance for representation, forecasting, and planning in online scenarios. However, most SSMs are designed for discrete-time sequences and inapplicable when the observations are irregular in time. To solve the problem, we propose a novel continuous-time SSM named Ordinary Differential Equation Recurrent State Space Model (ODE-RSSM). ODE-RSSM incorporates an ordinary differential equation (ODE) network (ODE-Net) to model the continuous-time evolution of latent states between adjacent time points. Inspired from the equivalent linear transformation on integration limits, we propose an efficient reparameterization method for solving batched ODEs with non-uniform time spans in parallel for efficiently training the ODE-RSSM with irregularly sampled sequences. We also conduct extensive experiments to evaluate the proposed ODE-RSSM and the baselines on three input-output datasets, one of which is a rollout of a private industrial dataset with strong long-term delay and stochasticity. The results demonstrate that the ODE-RSSM achieves better performance than other baselines in open loop prediction even if the time spans of predicted points are uneven and the distribution of length is changeable. Code is availiable at https://github.com/yuanzhaolin/ODE-RSSM.

PDF Details DOI