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Lu Zhou

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6 papers

2 author rows

IROS Conference 2025 Conference Paper

Automated Dual-Micropipette Coordination Microinjection for Batch Zebrafish Larvae Based on Pose Estimation

Can Wang
Rongxin Liu
Huiying Gong
Zengshuo Wang
Lu Zhou
Yaowei Liu
Xin Zhao 0010
Mingzhu Sun

Zebrafish are widely used in the biomedical field, as an ideal model for microinjection. In automated zebrafish microinjection, posture adjustment is the first and key step, which takes a lot of skill, and injection success assessment is a challenging task. Constrained by these two aspects, it is difficult to further enhance the efficiency and success rate of injection. In this study, we propose an automated dual-micropipette coordination microinjection system. Zebrafish are randomly arranged in our system, reducing the operational difficulty, and the yolk is positioned using a pose estimation algorithm, followed by injection accomplished with dual-micropipette. Due to the reduction of posture adjustment time by half, the proposed system achieves the shortest injection time of 15. 2s. Moreover, the simplicity of the system and the ease of operation contribute to the clinical feasibility of our system.

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AAAI Conference 2025 Conference Paper

Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration Path

Yuchen Ren
Zhengyu Zhao
Chenhao Lin
Bo Yang
Lu Zhou
Zhe Liu
Chao Shen

Transferable adversarial examples are known to cause threats in practical, black-box attack scenarios. A notable approach to improving transferability is using integrated gradients (IG), originally developed for model interpretability. In this paper, we find that existing IG-based attacks have limited transferability due to their naive adoption of IG in model interpretability. To address this limitation, we focus on the IG integration path and refine it in three aspects: multiplicity, monotonicity, and diversity, supported by theoretical analyses. We propose the Multiple Monotonic Diversified Integrated Gradients (MuMoDIG) attack, which can generate highly transferable adversarial examples on different CNN and ViT models and defenses. Experiments validate that MuMoDIG outperforms the latest IG-based attack by up to 37.3% and other state-of-the-art attacks by 8.4%. In general, our study reveals that migrating established techniques to improve transferability may require non-trivial efforts.

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AAAI Conference 2023 Conference Paper

Auto-Weighted Multi-View Clustering for Large-Scale Data

Xinhang Wan
Xinwang Liu
Jiyuan Liu
Siwei Wang
Yi Wen
Weixuan Liang
En Zhu
Zhe Liu

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factorization-based models are a representative of solving this problem. However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability. Moreover, a series of large-scale algorithms that bear one or more hyperparameters are impractical in real-world applications. To address the two issues, we propose an auto-weighted multi-view clustering (AWMVC) algorithm. Specifically, AWMVC first learns coefficient matrices from corresponding base matrices of different dimensions, then fuses them to obtain an optimal consensus matrix. By mapping original features into distinctive low-dimensional spaces, we can attain more comprehensive knowledge, thus obtaining better clustering results. Moreover, we design a six-step alternative optimization algorithm proven to be convergent theoretically. Also, AWMVC shows excellent performance on various benchmark datasets compared with existing ones. The code of AWMVC is publicly available at https://github.com/wanxinhang/AAAI-2023-AWMVC.

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AAAI Conference 2023 Conference Paper

Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering

Pei Zhang
Siwei Wang
Liang Li
Changwang Zhang
Xinwang Liu
En Zhu
Zhe Liu
Lu Zhou

In the past few years, numerous multi-view graph clustering algorithms have been proposed to enhance the clustering performance by exploring information from multiple views. Despite the superior performance, the high time and space expenditures limit their scalability. Accordingly, anchor graph learning has been introduced to alleviate the computational complexity. However, existing approaches can be further improved by the following considerations: (i) Existing anchor-based methods share the same number of anchors across views. This strategy violates the diversity and flexibility of multi-view data distribution. (ii) Searching for the optimal anchor number within hyper-parameters takes much extra tuning time, which makes existing methods impractical. (iii) How to flexibly fuse multi-view anchor graphs of diverse sizes has not been well explored in existing literature. To address the above issues, we propose a novel anchor-based method termed Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering (FDAGF) in this paper. Instead of manually tuning optimal anchor with massive hyper-parameters, we propose to optimize the contribution weights of a group of pre-defined anchor numbers to avoid extra time expenditure among views. Most importantly, we propose a novel hybrid fusion strategy for multi-size anchor graphs with theoretical proof, which allows flexible and diverse anchor graph fusion. Then, an efficient linear optimization algorithm is proposed to solve the resultant problem. Comprehensive experimental results demonstrate the effectiveness and efficiency of our proposed framework. The source code is available at https://github.com/Jeaninezpp/FDAGF.

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AAAI Conference 2020 Conference Paper

Progressive Bi-C3D Pose Grammar for Human Pose Estimation

Lu Zhou
Yingying Chen
Jinqiao Wang
Hanqing Lu

In this paper, we propose a progressive pose grammar network learned with Bi-C3D (Bidirectional Convolutional 3D) for human pose estimation. Exploiting the dependencies among the human body parts proves effective in solving the problems such as complex articulation, occlusion and so on. Therefore, we propose two articulated grammars learned with Bi-C3D to build the relationships of the human joints and exploit the contextual information of human body structure. Firstly, a local multi-scale Bi-C3D kinematics grammar is proposed to promote the message passing process among the locally related joints. The multi-scale kinematics grammar excavates different levels human context learned by the network. Moreover, a global sequential grammar is put forward to capture the long-range dependencies among the human body joints. The whole procedure can be regarded as a local-global progressive reﬁnement process. Without bells and whistles, our method achieves competitive performance on both MPII and LSP benchmarks compared with previous methods, which conﬁrms the feasibility and effectiveness of C3D in information interactions.

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KER Journal 2020 Journal Article

Towards evaluating complex ontology alignments

Lu Zhou
Elodie Thiéblin
Michelle Cheatham
Daniel Faria
Catia Pesquita
Cassia Trojahn
Ondřej Zamazal

Abstract The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.

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