Lei Zhang

Modified analytic solution for elliptical liquid inclusions in a soft solid capturing partial geometric nonlinearity

Molin Sun, Ming Dai, Jian Hua, Lei Zhang

Soft materials embedded with liquid inclusions have been widely applied in the fields of biomedical engineering, flexible electronics and soft robotics owing to their unique mechanical properties, while classical linear elasticity solutions fail to accurately characterize their n…Read more

Comparative analysis of intracranial pressure prediction models using optic nerve sheath diameter and additional ultrasound metrics

Mingqing cheng, Tingting Liu, Geng Guo, Zili Hui, Lei Zhang, J B Hu, et al.

Optic nerve sheath diameter (ONSD) assessment of elevated intracranial pressure is increasingly emphasised as a noninvasive modality. Nevertheless, quantitative assessment remains limited, and study criteria vary. We constructed four prediction models based on ONSD and compared t…Read more

Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform

Zhongchen Zhao, Jixin Wang, Qi Xie, Hui Lin, Lei Zhang, Deyu Meng, et al.

Equivariant networks embed geometric symmetries as structural priors through weight sharing, achieving remarkable parameter efficiency across vision tasks. However, this parameter efficiency does not translate into compute efficiency: existing implementations unroll the structure…Read more

Handwritten and Printed Text Segmentation via Region-Aware Human-Writing Descriptor Engineering

Zhixian Lu, Jianwei Zhang, Lei Zhang, Fei Yuan, Jin Wang, Chang Liu, et al.

With the increasing demand for reusing paper documents in educational and office settings, accurate segmentation of handwritten and printed text has become a crucial step in document digitization. Although numerous deep learning models have been developed for this task, their hig…Read more

One-Shot Federated Learning With Lightweight Intermediate Models in Wireless Sensor Network Setting

Yexin Dou, Haijiang Wang, Jian Wan, Lei Zhang, Jie Huang, Yi Liu

MBTI: A Multi-Branch Efficient Fine-Tuning Framework for Hyperspectral Image Classification with Foundation Models

Mingzhen Xu, Haonan Guo, Di Wang, Yinghua Qu, Zhiliang Zhou, Lei Zhang, et al.

Hyperspectral foundation models learn transferable spectral-spatial representations from large-scale unlabeled data. They provide an effective paradigm for adapting to downstream hyperspectral image (HSI) classification tasks with limited labeled samples. However, spectral band c…Read more

IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment

Jinjian Wu, Jiaqi Tang, Wei Wei, Yingying Yan, Jianmin Chen, Botong Geng, et al.

Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability. Recent approaches based on multimodal large language models (MLLMs) introduce textual reasoning for quality prediction, yet their judgments rely heavi…Read more

FlowPainter: Inpainting Optical Flow via Confidence-Guided Completion

Yuang Meng, Chenyang Wu, Xianshun Liu, Chun-Le Guo, Zichen Liang, Lina Lei, et al.

Existing optical flow methods broadly follow two paradigms: iterative optimization and diffusion-based estimation. Iterative methods, exemplified by RAFT, achieve high accuracy through recurrent refinement, but remain challenged by large displacements and complex motion. Diffusio…Read more

Lean-QIT: Towards a Formal Infrastructure for Quantum Information Theory

Chengkai Zhu, Ziao Tang, Guocheng Zhen, Yimeng Cao, Yusheng Zhao, Ranyiliu Chen, et al.

Quantum information theory (QIT) characterizes the capabilities and fundamental limits of quantum information processing, underpinning quantum communication, computation, and error correction. Formalizing its coding theorems requires connecting finite-block protocols, analytic in…Read more

SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models

Changti Wu, Bin Yu, Zhaolong Shen, Shijie Lian, Xiaopeng Lin, Cong Huang, et al.

Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not necessarily yield better policies due to redundancy, noise, and uneven coverage. Existing data selection methods often assess demons…Read more

Perceiving Better Moments: Cover Frame Reselection and Enhancement for Live Photos with the Live2K Dataset

Junyu Lou, Kai Chen, Weiyi You, Hui Zeng, Lei Zhang, Shuhang Gu

Modern smartphones capture Live Photos, short video bursts surrounding a still image, offering a dynamic and engaging photographic experience. However, the cover photo and video components are generated by two distinct imaging pipelines: the photo stream undergoes full computatio…Read more

ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion

Yao Liu, Lishen Qu, Shihao Zhou, Jie Liang, Hui Zeng, Yabin Peng, et al.

Multi-Exposure Fusion (MEF) effectively extends dynamic range, but practical deployment is hindered by motion-induced ghosting and the scarcity of high-quality dynamic benchmarks. Current benchmarks largely neglect dynamic scenes and lack reliable ground truth, making it difficul…Read more

Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval

Jingjing Zhang, Lei Zhang, Zheren Fu, Zhendong Mao

Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification. While supervised CIR relies on costly triplets, Zero-Shot CIR (ZS-CIR) alleviates this reliance through proxy tasks trained on image-text pairs. However, existing proxy task…Read more

BP-TTA: Balanced and Prototype-Guided Test-Time Adaptation in Dynamic Scenarios

Shaoyang Huang, Yashi Zhu, Yichen Yu, Lei Zhang, Zhang Yi, Tao He

Test-Time Adaptation (TTA) enables models trained on a source domain to adapt online to unlabeled test data under distribution shifts. While recent TTA methods have moved beyond static settings and begun to consider continual domain shifts, they primarily address distribution dri…Read more

ERA: Entropy-Guided Visual Token Pruning with Rectified Attention for Efficient MLLMs

Yuhao Wang, Mu Qiao, Haiwen Diao, Yunzhi Zhuge, Pingping Zhang, Xindong Zhang, et al.

Multimodal Large Language Models (MLLMs) incur prohibitive inference costs due to long visual token sequences. Training-free visual token reduction provides an efficient solution. However, existing methods distort attention distributions, giving rise to a phenomenon we term Atten…Read more

DRIFT: Difficulty Routing Self-DIstillation with Rhythm-Gated Exploration and Success BuFfer Training

Haisen Luo, Yiwei Liu, Haoning Wang, Dan Liu, Junxi Yin, Haotian Wang, et al.

Enabling large language models to achieve stable self-improvement without external expert supervision remains a central challenge in complex reasoning tasks. Existing self-distillation and reinforcement learning methods lack explicit mechanisms for tracking problem-level learning…Read more

Harvesting AI Computation at the Edge via Generic Approximation

Yihan Wang, Huiru Yan, Luxin Zhang, Long Cheng, Weiwei Chen, Ying Wang, et al.

With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge. These chips are typically specialized for structured neural network (NN) processing and are designed to meet peak workload demand…Read more

There and Back Again: A Flexible-Frame Transformer for Multi-Exposure Fusion

Lishen Qu, Yao Liu, Shihao Zhou, Jie Liang, Hui Zeng, Lei Zhang, et al.

Multi-exposure fusion (MEF) brings the dynamic range of conventional cameras closer to that of human vision, producing images with rich scene content. Given the large variability in scene luminance, exposure strategies often require different numbers of frames to capture the full…Read more

PLAA: Packet-level Adversarial Attacks in Network Traffic Detection

Jinhao You, Zan Zhou, Shujie Yang, Yi Sun, Lei Zhang, Changqiao Xu

Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy. However, DNNs are highly susceptible to adversarial attacks, which generate malicious traffic to evade NIDS detection. Existing approaches often adapt adv…Read more

Federated Deep Reinforcement Learning-Based Energy Efficiency Optimization for Closed-Loop Operations and Maintenance in Autonomous Communication Networks

Haitao Li, Donglei Xu, Quanfeng Yao, Lei Zhang, Hui Wan, Xianjun Peng, et al.

With the evolution toward 5G-Advanced and 6G autonomous communication networks, achieving energy-efficient closed-loop operations and maintenance (O&M) has become increasingly challenging due to large-scale deployment, heterogeneous network elements, and highly dynamic traffi…Read more

An Intrusion Detection Method Based on Hybrid Machine Learning and Neural Network in the Industrial Control Field

Duo Sun, Lei Zhang, Kai Jin, Jiasheng Ling, Xiaoyuan Zheng

Aiming at the imbalance of industrial control system data and the poor detection effect of industrial control intrusion detection systems on network attack traffic problems, we propose an ETM-TBD model based on hybrid machine learning and neural network models. Aiming at the prob…Read more