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Zhenyu Li

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13 papers

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EAAI Journal 2026 Journal Article

A cosmetic packaging design method based on online reviews

Zhan Gao
Zhenyu Li

To address the transformation of user experience and packaging iteration in cosmetics due to the diversification of usage scenarios and demands, this study capitalizes on the advancements in artificial intelligence across user analysis, data analysis, and generative design domains, and proposes a cosmetic packaging design approach centered around online reviews. In this study, 124, 879 pieces of user review data were collected from JingDong (JD), a Chinese e-commerce platform, using Python programming technology. Five topics are clustered through the application of the Latent Dirichlet Allocation (LDA) topic model. By integrating the coding of Grounded Theory, 18 demand elements within six core categories are summarized. The Kano model and the Analytic Hierarchy Process (AHP) are employed to classify and rank these demands. Notably, aspects such as strong brand recognition (M1, 0. 2182), strong brand value perception (M5, 0. 1129), and visually appealing and refined aesthetics (A5, 0. 0983) exhibit relatively high weights. Subsequently, six lipstick packaging design schemes are developed by combining traditional software with the MidJourney generative artificial intelligence tool. Through comprehensive evaluation using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, the optimal Scheme c is identified and further optimized. This study constructs a comprehensive design strategy with user online reviews at its core, encompassing data collection, analysis, scheme design, artificial intelligence (AI)-assisted design, and evaluation. It is recommended that the application of artificial intelligence (AI)-assisted design be significantly enhanced throughout the entire design process, enabling precise and rapid generation of design schemes, streamlining the process, and shortening the development cycle.

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AAAI Conference 2026 Conference Paper

EdgeMTSC: A Lightweight Large-Kernel ConvNet for Multivariate Time Series Classification

Xueyi Zhou
Zhenyu Li
Dong-Kyu Chae

In large-scale sensor networks, Multivariate Time Series Classification (MTSC) is a pivotal task for identifying events dependent on longitudinal data at the edge. However, existing methods focus on neither the inherent ability of convolutional networks to perceive subsequence features, nor the prolonged processing pipeline and the model deployment overhead brought by the highly parameterized models. To resolve these difficulties, we present EdgeMTSC, a lightweight large-kernel ConvNet for MTSC, which naturally extracts and learns features of diverse subsequences. Specifically, a novel module named Inter-channel Message Passing-driven Kernel Block (IMP-KB) is proposed, which maintains a learnable correlation matrix to propagate and merge inter-channel messages, and fuses miscellaneous patterns learned by parallel conv kernels of different sizes. EdgeMTSC sequences two modules of different receptive fields to aggregate local features using small kernels and study long-term representation provided by large kernels, respectively. For inference parameter reduction and accelerating inference without performance loss, the conv blocks in IMP-KBs are structurally reparameterizable. The performance of our model (76.2%) is benchmarked on 26 UEA MTSC datasets and is superior to the SOTA model (MPTSNet, 75%). At the same time, EdgeMTSC uses the fewest parameters and achieves the minimum inference time, applicable on any machine (8 devices ranging from large-scale distributed AI computing servers to resource-constrained edge devices) and in any application scenario.

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IROS Conference 2025 Conference Paper

Bridging Text and Vision: A Multi-View Text-Vision Registration Approach for Cross-Modal Place Recognition

Tianyi Shang
Zhenyu Li
Pengjie Xu
Jinwei Qiao
Gang Chen
Zihan Ruan
Weijun Hu

Mobile robots necessitate advanced natural language understanding capabilities to accurately identify locations and perform tasks such as package delivery. However, traditional visual place recognition (VPR) methods rely solely on single-view visual information and cannot interpret human language descriptions. To overcome this challenge, we bridge text and vision by proposing a multiview (360° views of the surroundings) text-vision registration approach called Text4VPR for place recognition task, which is the first method that exclusively utilizes textual descriptions to match a database of images. Text4VPR employs the frozen T5 language model to extract global textual embeddings. Additionally, it utilizes the Sinkhorn algorithm with temperature coefficient to assign local tokens to their respective clusters, thereby aggregating visual descriptors from images. During the training stage, Text4VPR emphasizes the alignment between individual text-image pairs for precise textual description. In the inference stage, Text4VPR uses the Cascaded Cross-Attention Cosine Alignment (CCCA) to address the internal mismatch between text and image groups. Subsequently, Text4VPR performs precisely place match based on the descriptions of text-image groups. On Street360Loc, the first text to image VPR dataset we created, Text4VPR builds a robust baseline, achieving a leading top-1 accuracy of 56% and a leading top-10 accuracy of 91% within a 5-meter radius on the test set, which indicates that localization from textual descriptions to images is not only feasible but also holds significant potential for further advancement, as shown in Figure 1. Our code is available at https://github.com/nuozimiaowu/Text4VPR.

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AAAI Conference 2025 Conference Paper

COMM: Concentrated Margin Maximization for Robust Document-Level Relation Extraction

Zhichao Duan
Tengyu Pan
Zhenyu Li
Xiuxing Li
Jianyong Wang

Document-level relation extraction (DocRE) is the process of identifying and extracting relations between entities that span multiple sentences within a document. Due to its realistic settings, DocRE has garnered increasing research attention in recent years. Previous research has mostly focused on developing sophisticated encoding models to better capture the intricate patterns between entity pairs. While these advancements are undoubtedly crucial, an even more foundational challenge lies in the data itself. The complexity inherent in DocRE makes the labeling process prone to errors, compounded by the extreme sparsity of positive relation samples, which is driven by both the limited availability of positive instances and the broad diversity of positive relation types. These factors can lead to biased optimization processes, further complicating the task of accurate relation extraction. Recognizing these challenges, we have developed a robust framework called COMM to better solve DocRE. COMM operates by initially employing an instance-aware reasoning method to dynamically capture pertinent information of entity pairs within the document and extract relational features. Following this, COMM takes into account the distribution of relations and the difficulty of samples to dynamically adjust the margins between prediction logits and the decision threshold, a process we call Concentrated Margin Maximization. In this way, COMM not only enhances the extraction of relevant relational features but also boosts DocRE performance by addressing the specific challenges posed by the data. Extensive experiments and analysis demonstrate the versatility and effectiveness of COMM, especially its robustness when trained on low-quality data (achieves >10% performance gains).

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AAAI Conference 2025 Conference Paper

CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction

Rong Han
Xiaohong Liu
Tong Pan
Jing Xu
Xiaoyu Wang
Wuyang Lan
Zhenyu Li
Zixuan Wang

Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the binding mechanisms comprehensively. The recent emerging pre-trained language models trained on massive unsupervised sequences of protein and RNA have shown strong representation ability for various in-domain downstream tasks, including binding site prediction. However, applying different-domain language models collaboratively for complex-level tasks remains unexplored. In this paper, we propose CoPRA to bridge pre-trained language models from different biological domains via Complex structure for Protein-RNA binding Affinity prediction. We demonstrate for the first time that cross-biological modal language models can collaborate to improve binding affinity prediction. We propose a Co-Former to combine the cross-modal sequence and structure information and a bi-scope pre-training strategy for improving Co-Former's interaction understanding. Meanwhile, we build the largest protein-RNA binding affinity dataset PRA310 for performance evaluation. We also test our model on a public dataset for mutation effect prediction. CoPRA reaches state-of-the-art performance on all the datasets. We provide extensive analyses and verify that CoPRA can (1) accurately predict the protein-RNA binding affinity; (2) understand the binding affinity change caused by mutations; and (3) benefit from scaling data and model size.

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IROS Conference 2025 Conference Paper

MambaPlace: Text-to-Point-Cloud Cross-Modal Place Recognition with Attention Mamba Mechanisms

Tianyi Shang
Zhenyu Li
Pengjie Xu
Jinwei Qiao

Vision-Language Place Recognition (VLPR) enhances robot localization performance by incorporating natural language descriptions from images. By utilizing language information, VLPR directs robot place matching, overcoming the constraint of solely depending on vision. However, general multimodal information integration methods are not well equipped to capture the dynamics of cross-modal interactions, especially in the presence of complex intra-modal and inter-modal correlations. To this end, this paper proposes a novel coarse-to-fine and end-to-end connected cross-modal place recognition framework, called MambaPlace. In the coarse-localization stage, the text description and 3D point cloud are encoded by the pre-trained T5 and instance encoder, respectively. They are then processed using Text-Attention Mamba (TAM) and Point Cloud Multi-Strategy Scanning Mamba (MSSM), with the latter mimicking the eye’s focusing mechanism, for data enhancement and alignment. In the subsequent fine-localization stage, the features of the text description and 3D point cloud are cross-modally fused and further enhanced through Cascaded Cross-Attention Mamba (CCAM). Finally, we predict the positional offset from the fused text-point cloud features, achieving the most accurate localization. Extensive experiments show that MambaPlace achieves improved localization accuracy on the KITTI360Pose dataset compared to the state-of-the-art methods. Specifically, as shown in Fig. 1, when ϵ<5, MambaPlace achieves 5% higher test accuracy compared to the existing state-of-the-art.

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NeurIPS Conference 2025 Conference Paper

XIFBench: Evaluating Large Language Models on Multilingual Instruction Following

Zhenyu Li
Kehai Chen
Yunfei Long
Xuefeng Bai
Yaoyin Zhang
Xuchen Wei
Juntao Li
Min Zhang

Large Language Models (LLMs) have demonstrated remarkable instruction-following capabilities across various applications. However, their performance in multilingual settings lacks systematic investigation, with existing evaluations lacking fine-grained constraint analysis across diverse linguistic contexts. We introduce XIFBench, a comprehensive constraint-based benchmark for evaluating multilingual instruction-following abilities of LLMs, comprising 558 instructions with 0-5 additional constraints across five categories ( Content, Style, Situation, Format, and Numerical ) in six languages spanning different resource levels. To support reliable and consistent cross-lingual evaluation, we implement three methodological innovations: cultural accessibility annotation, constraint-level translation validation, and requirement-based evaluation using English requirements as semantic anchors across languages. Extensive experiments with various LLMs not only quantify performance disparities across resource levels but also provide detailed insights into how language resources, constraint categories, instruction complexity, and cultural specificity influence multilingual instruction-following. Our code and data are available at https: //github. com/zhenyuli801/XIFBench.

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AAAI Conference 2024 Conference Paper

FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering

Zhenyu Li
Sunqi Fan
Yu Gu
Xiuxing Li
Zhichao Duan
Bowen Dong
Ning Liu
Jianyong Wang

Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users. Unfortunately, the performance of most KBQA models tends to decline significantly in real-world scenarios where high-quality annotated data is insufficient. To mitigate the burden associated with manual annotation, we introduce FlexKBQA by utilizing Large Language Models (LLMs) as program translators for addressing the challenges inherent in the few-shot KBQA task. Specifically, FlexKBQA leverages automated algorithms to sample diverse programs, such as SPARQL queries, from the knowledge base, which are subsequently converted into natural language questions via LLMs. This synthetic dataset facilitates training a specialized lightweight model for the KB. Additionally, to reduce the barriers of distribution shift between synthetic data and real user questions, FlexKBQA introduces an executionguided self-training method to iterative leverage unlabeled user questions. Furthermore, we explore harnessing the inherent reasoning capability of LLMs to enhance the entire framework. Consequently, FlexKBQA delivers substantial flexibility, encompassing data annotation, deployment, and being domain agnostic. Through extensive experiments on GrailQA, WebQSP, and KQA Pro, we observe that under the few-shot even the more challenging zero-shot scenarios, FlexKBQA achieves impressive results with a few annotations, surpassing all previous baselines and even approaching the performance of supervised models, achieving a remarkable 93% performance relative to the fully-supervised models. We posit that FlexKBQA represents a significant advancement towards exploring better integration of large and lightweight models. Code is available at https://github.com/leezythu/FlexKBQA.

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EAAI Journal 2024 Journal Article

Reinforcement learning-based distributed impedance control of robots for compliant operation in tight interaction tasks

Pengjie Xu
Zhenyu Li
Xun Liu
Tianrui Zhao
Lin Zhang
Yanzheng Zhao

It is challenging to achieve compliant operation in tight interaction tasks, where closed-loop constraints are formed between the robots or between a robot and the environment. Complex dynamic interactions and uncertain parameters degrade the performance of model-based controllers. In this paper, a reinforcement learning-based distributed impedance control approach is proposed for these tight interaction tasks. Two aspects are considered to ensure compliant operation for the robots. First, a distributed impedance model is established through the design of reasonable independence states and nodes networks. Second, the reinforcement learning agent is designed to make decision for adjusting impedance parameters. The trained parameters are integrated into the designed impedance model, and a model-based controller is then employed for compliant control for the robots. The effectiveness is validated under two different tight interaction scenarios via co-simulation. Compliant operation can be achieved whether between robots or between a robot and the environment.

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IJCAI Conference 2022 Conference Paper

AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection

Zehui Chen
Zhenyu Li
Shiquan Zhang
Liangji Fang
Qinhong Jiang
Feng Zhao
Bolei Zhou
Hang Zhao

Object detection through either RGB images or the LiDAR point clouds has been extensively explored in autonomous driving. However, it remains challenging to make these two data sources complementary and beneficial to each other. In this paper, we propose AutoAlign, an automatic feature fusion strategy for 3D object detection. Instead of establishing deterministic correspondence with camera projection matrix, we model the mapping relationship between the image and point clouds with a learnable alignment map. This map enables our model to automate the alignment of non-homogenous features in a dynamic and data-driven manner. Specifically, a cross-attention feature alignment module is devised to adaptively aggregate pixel-level image features for each voxel. To enhance the semantic consistency during feature alignment, we also design a self-supervised cross-modal feature interaction module, through which the model can learn feature aggregation with instance-level feature guidance. Extensive experimental results show that our approach can lead to 2. 3 mAP and 7. 0 mAP improvements on the KITTI and nuScenes datasets respectively. Notably, our best model reaches 70. 9 NDS on the nuScenes testing leaderboard, achieving competitive performance among various state-of-the-arts.

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AAAI Conference 2022 Conference Paper

SimIPU: Simple 2D Image and 3D Point Cloud Unsupervised Pre-training for Spatial-Aware Visual Representations

Zhenyu Li
Zehui Chen
Ang Li
Liangji Fang
Qinhong Jiang
Xianming Liu
Junjun Jiang
Bolei Zhou

Pre-training has become a standard paradigm in many computer vision tasks. However, most of the methods are generally designed on the RGB image domain. Due to the discrepancy between the two-dimensional image plane and the three-dimensional space, such pre-trained models fail to perceive spatial information and serve as sub-optimal solutions for 3D-related tasks. To bridge this gap, we aim to learn a spatial-aware visual representation that can describe the three-dimensional space and is more suitable and effective for these tasks. To leverage point clouds, which are much more superior in providing spatial information compared to images, we propose a simple yet effective 2D Image and 3D Point cloud Unsupervised pre-training strategy, called SimIPU. Specifically, we develop a multi-modal contrastive learning framework that consists of an intra-modal spatial perception module to learn a spatial-aware representation from point clouds and an inter-modal feature interaction module to transfer the capability of perceiving spatial information from the point cloud encoder to the image encoder, respectively. Positive pairs for contrastive losses are established by the matching algorithm and the projection matrix. The whole framework is trained in an unsupervised end-toend fashion. To the best of our knowledge, this is the first study to explore contrastive learning pre-training strategies for outdoor multi-modal datasets, containing paired camera images and LIDAR point clouds.

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YNICL Journal 2022 Journal Article

Use of magnetic resonance elastography to gauge meningioma intratumoral consistency and histotype

Yu Shi
Yunlong Huo
Chen Pan
Yafei Qi
Ziying Yin
Richard L. Ehman
Zhenyu Li
Xiaoli Yin

OBJECTIVE: To determine whether tumor shear stiffness, as measured by magnetic resonance elastography, corresponds with intratumoral consistency and histotype. MATERIALS AND METHODS: A total of 88 patients with 89 meningiomas (grade 1, 74 typical [13 fibroblastic, 61 non-fibroblastic]; grade 2, 12 atypical; grade 3, 3 anaplastic) were prospectively studied, each undergoing preoperative MRE in conjunction with T1-, T2- and diffusion-weighted imaging. Contrast-enhanced T1-weighted sequences were also obtained. Tumor consistency was evaluated as heterogeneous or homogenous, and graded on a 5-point scale intraoperatively. MRE-determined shear stiffness was associated with tumor consistency by surgeon's evaluation and whole-slide histologic analyses. RESULTS: Mean tumor stiffness overall was 3.81+/-1.74 kPa (range, 1.57-12.60 kPa), correlating well with intraoperative scoring (r = 0.748; p = 0.001). MRE performed well as a gauge of tumor consistency (AUC = 0.879, 95 % CI: 0.792-0.938) and heterogeneity (AUC = 0.773, 95 % CI: 0.618-0.813), significantly surpassing conventional MR techniques (DeLong test, all p 0.05). Small (<2.5 cm, n = 6) and intraventricular (n = 2) tumors displayed inconsistencies between MRE and surgeon's evaluation. CONCLUSIONS: The results of this prospective study provide substantial evidence that preoperative evaluation of meningiomas with MRE can reliably characterize tumor stiffness and spatial heterogeneity to aid neurosurgical planning.

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ICRA Conference 2021 Conference Paper

Motion-Aware Robotic 3D Ultrasound

Zhongliang Jiang
Hanyu Wang 0007
Zhenyu Li
Matthias Grimm
Mingchuan Zhou
Ulrich Eck
Sandra V. Brecht
Tim C. Lueth

Robotic three-dimensional (3D) ultrasound (US) imaging has been employed to overcome the drawbacks of traditional US examinations, such as high inter-operator variability and lack of repeatability. However, object movement remains a challenge as unexpected motion decreases the quality of the 3D compounding. Furthermore, attempted adjustment of objects, e. g. , adjusting limbs to display the entire limb artery tree, is not allowed for conventional robotic US systems. To address this challenge, we propose a vision-based robotic US system that can monitor the object’s motion and automatically update the sweep trajectory to provide 3D compounded images of the target anatomy seamlessly. To achieve these functions, a depth camera is employed to extract the manually planned sweep trajectory after which the normal direction of the object is estimated using the extracted 3D trajectory. Subsequently, to monitor the movement and further compensate for this motion to accurately follow the trajectory, the position of firmly attached passive markers is tracked in real-time. Finally, a stepwise compounding was performed. The experiments on a gel phantom demonstrate that the system can resume a sweep when the object is not stationary during scanning.

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