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Lulu Wang

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4 papers

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JBHI Journal 2026 Journal Article

Displacement-Guided Anisotropic 3D-MRI Super-Resolution With Warp Mechanism

Lulu Wang
Siyi Liu
Zhengtao Yu
Jinglong Du
Yingna Li

Enhancing the resolution of Magnetic Resonance Imaging (MRI) through super-resolution (SR) reconstruction is crucial for boosting diagnostic precision. However, current SR methods primarily rely on single LR images or multi-contrast features, limiting detail restoration. Inspired by video frame interpolation, this work utilizes the spatiotemporal correlations between adjacent slices to reformulate the SR task of anisotropic 3D-MRI image into the generation of new high-resolution (HR) slices between adjacent 2D slices. The generated SR slices are subsequently combined with the HR adjacent slices to create a new HR 3D-MRI image. We propose a innovative network architecture termed DGWMSR, comprising a backbone network and a feature supplement module (FSM). The backbone’s core innovations include the displacement former block (DFB) module, which independently extracts structural and displacement features, and the mask-displacement vector network (MDVNet) which combines with Warp mechanism to facilitate edge pixel detailing. The DFB integrates the inter-slice attention (ISA) mechanism into the Transformer, effectively minimizing the mutual interference between the two types of features and mitigating volume effects during reconstruction. Additionally, the FSM module combines self-attention with feed-forward neural network, which emphasizes critical details derived from the backbone architecture. Experimental results demonstrate the DGWMSR network outperforms current MRI SR methods on Kirby21, ANVIL-adult, and MSSEG datasets.

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AAAI Conference 2025 Conference Paper

Addressing Cold-Start Problem in Click-Through Rate Prediction via Supervised Diffusion Modeling

Wenqiao Zhu
Lulu Wang
Jun Wu

Predicting Click-Through Rates is a crucial function within recommendation and advertising platforms, as the output of CTR prediction determines the order of items shown to users. The Embedding and MLP paradigm has become a standard approach for industrial recommendation systems and has been widely deployed. However, this paradigm suffers from cold-start problems, where there is either no or only limited user action data available, leading to poorly learned ID embeddings. The cold-start problem hampers the performance of new items. To address this problem, we design a novel diffusion model to generate a warmed-up embedding for new items. Specifically, we define a novel diffusion process between the ID embedding space and the side information space. In addition, we can derive a sub-sequence from the diffusion steps to expedite training, given that our diffusion model is non-Markovian. Our diffusion model is supervised by both the variational inference and binary cross-entropy objectives, enabling it to generate warmed-up embeddings for items in both the cold-start and warm-up phases. Additionally, we have conducted extensive experiments on three recommendation datasets. The results confirmed the effectiveness of our approach.

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EAAI Journal 2025 Journal Article

Distribution-aware network with context and entity attention for scene graph generation

Tongling Pan
Lulu Wang
Ruoyu Zhang
Zhengtao Yu
Yingna Li

Scene Graph Generation (SGG) aims to detect objects and infer their pairwise relationships in images, forming a structured semantic graph. Despite recent advances, existing methods struggle with effectively capturing rich contextual dependencies and suffer from biased relation prediction due to long-tail data distributions. To address these challenges, we propose a novel SGG framework, named DANCE, which integrates three key modules: the Context-Augmented Message Passing (CAMP) module, the Distribution-aware Dynamic Weighted Loss (DDW-Loss), and the Dual-Entity Attention Enhancement (DEAE) mechanism. Specifically, the CAMP module leverages a Gated Recurrent Unit (GRU) based architecture to reason over the scene graph, capturing entity interactions through sequential context encoding. To mitigate noise from random graph connections, we incorporate multi-head attention into GRU and fuse its output with initial visual features via residual connections. This design enhances context propagation while maintaining stability. Furthermore, the DDW-Loss function dynamically adjusts the loss weights of relation categories based on their frequency distribution, thus improving the learning of semantically meaningful but infrequent relations. Finally, the DEAE module employs parallel multi-head attention over subject and object features, enabling the model to extract fine-grained semantic dependencies and generate more discriminative relational embeddings. Experimental results on three popular datasets demonstrate that our method significantly improves the performance of scene graph generation tasks and outperforms existing methods. In addition, the proposed method exhibits strong adaptability and robustness in real-world scenarios characterized by complex scenes, noisy inputs, and varying image resolutions, highlighting its superior generalization capability for practical deployment.

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IS Journal 2022 Journal Article

Improved Ensemble Classification for Evolving Data Streams

Hui Tian
Lulu Wang
Hong Shen
Alan Wee-Chung Liew

A major challenge for evolving data stream classification is feature evolution where features of stream instances are dynamically changing as they progress. Existing classification methods considered feature evolution either for fixed-size data or of limited degree with presumed dependence to history, making them unable to work effectively on evolving data streams of unbounded size and arbitrary feature evolution. Particularly, for evolving data streams containing instances of multiple labels, classification coping with feature evolution faces significant challenges. In this article, we present efficient ensemble methods for classifying evolving data streams of both single label and multiple labels through effective model coupling. For single-label classification, we present an improved unsupervised classification algorithm that applies multi-cluster feature selection (MCFS), which was originally proposed for static data classification, in the DXMiner framework to handle each window of instances in a dynamic stream. Our method generates an optimal feature subset and achieves a high classification accuracy. We further improve the time complexity of the feature selection process in MCFS by applying the Ball-tree searching technique. For multi-label classification, we propose an effective fixed-size ensemble classifier based on multi-label KNN, which works only for static multi-label data classification, by incorporating a weight adaptation strategy among the classifiers in the ensemble to dynamically update the model and cope with arbitrary feature evolution of stream instances as the stream progresses. Extensive experiment results on real-life data streams show that our algorithms outperform the existing results for single-label and multi-label classification in classification accuracy and efficiency.

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