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Jinlin Wu

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

6DAttack: Backdoor Attacks in the 6DoF Pose Estimation

Jihui Guo
Zongmin Zhang
Zhen Sun
Yuhao Yang
Jinlin Wu
Fu Zhang
Xinlei He

Recent advances in deep learning have enabled highly accurate six-degree-of-freedom (6DoF) object pose estimation, leading to its widespread use in real-world applications such as robotics, augmented reality, virtual reality, and autonomous systems. However, backdoor attacks pose a major security risk to deep learning models. By injecting malicious triggers into training data, an attacker can cause a model to perform normally on benign inputs but behave incorrectly under specific conditions. While most research on backdoor attacks has focused on 2D vision tasks, their impact on 6DoF pose estimation remains largely unexplored. Furthermore, unlike traditional backdoors that only change the object class, backdoors against 6DoF pose estimation must additionally control continuous pose parameters, such as translation and rotation, making existing 2D backdoor attack methods not directly applicable to this setting. To address this gap, we propose a novel backdoor attack framework (6DAttack) that exposes vulnerabilities in 6DoF pose estimation. 6DAttack uses synthetic and real 3D objects of varying shapes as triggers and assigns target poses to induce controlled erroneous pose outputs while maintaining normal behavior on clean inputs. We evaluated this attack on multiple models (including PVNet, DenseFusion, and PoseDiffusion) and datasets (including LINEMOD, YCB-Video, and CO3D). Experimental results demonstrate that 6DAttack achieves extremely high attack success rates (ASRs) without compromising performance on legitimate tasks. Across various models and objects, the backdoored models achieve up to 100% ADD accuracy on clean data, while also achieving 100% ASR under trigger conditions. The accuracy of controlled erroneous pose output is also extremely high, with triggered samples achieving 97.70% ADD-P. These results demonstrate that the backdoor can be reliably implanted and activated, achieving a high ASR under trigger conditions while maintaining a negligible impact on benign data. Furthermore, we evaluate a representative defense and show that it remains ineffective under 6DAttack. Overall, our findings reveal a potentially serious and previously underexplored threat to modern 6DoF pose estimation models.

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AAAI Conference 2026 Conference Paper

MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models

Siqi Ma
Jiajie Huang
Fan Zhang
Jinlin Wu
Yue Shen
Guohui Fan
Zhu Zhang
Zelin Zang

Answering complex medical questions requires not only domain expertise and patient-specific information, but also structured and multi-perspective reasoning. Existing multi-agent approaches often rely on fixed roles or shallow interaction prompts, limiting their ability to detect and resolve fine-grained logical inconsistencies. To address this, we propose MedLA, a logic-driven multi-agent framework built on large language models. Each agent organizes its reasoning process into an explicit logical tree based on syllogistic triads (major premise, minor premise, and conclusion), enabling transparent inference and premise-level alignment. Agents engage in a multi-round, graph-guided discussion to compare and iteratively refine their logic trees, achieving consensus through error correction and contradiction resolution. We demonstrate that MedLA consistently outperforms both static role-based systems and single-agent baselines on challenging benchmarks such as MedDDx and standard medical QA tasks. Furthermore, MedLA scales effectively across both open-source and commercial LLM backbones, achieving state-of-the-art performance and offering a generalizable paradigm for trustworthy medical reasoning.

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ICRA Conference 2025 Conference Paper

Advancing Dense Endoscopic Reconstruction with Gaussian Splatting-Driven Surface Normal-Aware Tracking and Mapping

Yiming Huang 0007
Beilei Cui
Long Bai 0008
Zhen Chen 0018
Jinlin Wu
Zhen Li 0026
Hongbin Liu 0001
Hongliang Ren 0001

Simultaneous Localization and Mapping (SLAM) is essential for precise surgical interventions and robotic tasks in minimally invasive procedures. While recent advancements in 3D Gaussian Splatting (3DGS) have improved SLAM with high-quality novel view synthesis and fast rendering, these systems struggle with accurate depth and surface reconstruction due to multi-view inconsistencies. Simply incorporating SLAM and 3DGS leads to mismatches between the reconstructed frames. In this work, we present Endo-2DTAM, a real-time endoscopic SLAM system with 2D Gaussian Splatting (2DGS) to address these challenges. Endo-2DTAM incorporates a surface normal-aware pipeline, which consists of tracking, mapping, and bundle adjustment modules for geometrically accurate reconstruction. Our robust tracking module combines point-topoint and point-to-plane distance metrics, while the mapping module utilizes normal consistency and depth distortion to enhance surface reconstruction quality. We also introduce a pose-consistent strategy for efficient and geometrically coherent keyframe sampling. Extensive experiments on public endoscopic datasets demonstrate that Endo-2DTAM achieves an RMSE of $1. 87 \pm 0. 63 \mathbf{m m}$ for depth reconstruction of surgical scenes while maintaining computationally efficient tracking, high-quality visual appearance, and real-time rendering. Our code will be released at github.com/lastbasket/Endo-2DTAM.

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TMLR Journal 2025 Journal Article

MDTree: A Masked Dynamic Autoregressive Model for Phylogenetic Inference

Zelin Zang
ChenRui Duan
Siyuan Li
Jinlin Wu
BingoWing-Kuen Ling
Fuji Yang
Jiebo Luo
Zhen Lei

Phylogenetic tree inference requires optimizing both branch lengths and topologies, yet traditional MCMC-based methods suffer from slow convergence and high computational cost. Recent deep learning approaches improve scalability but remain constrained: Bayesian models are computationally intensive, autoregressive methods depend on fixed species orders, and flow-based models underutilize genomic signals. Fixed-order autoregression introduces an inductive bias misaligned with evolutionary proximity: early misplacements distort subsequent attachment probabilities and compound topology errors (exposure bias). Absent sequence-informed priors, the posterior over the super-exponential topology space remains diffuse and multimodal, yielding high-variance gradients and sluggish convergence for both MCMC proposals and neural samplers. We propose MDTree, a masked dynamic autoregressive framework that integrates genomic priors into a Dynamic Ordering Network to learn biologically informed node sequences. A dynamic masking mechanism further enables parallel node insertion, improving efficiency without sacrificing accuracy. Experiments on standard benchmarks demonstrate that MDTree outperforms existing methods in accuracy and runtime while producing biologically coherent phylogenies, providing a scalable solution for large-scale evolutionary analysis.

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ICRA Conference 2025 Conference Paper

SurgPLAN++: Universal Surgical Phase Localization Network for Online and Offline Inference

Zhen Chen 0018
Xingjian Luo
Jinlin Wu
Long Bai 0008
Zhen Lei 0001
Hongliang Ren 0001
Sébastien Ourselin
Hongbin Liu 0001

Surgical phase recognition is critical for assisting surgeons in understanding surgical videos. Existing studies focused more on online surgical phase recognition, by leveraging preceding frames to predict the current frame. Despite great progress, they formulated the task as a series of frame-wise classification, which resulted in a lack of global context of the entire procedure and incoherent predictions. Moreover, besides online analysis, accurate offline surgical phase recognition is also in significant clinical need for retrospective analysis, and existing online algorithms do not fully analyze the entire video, thereby limiting accuracy in offline analysis. To over-come these challenges and enhance both online and offline inference capabilities, we propose a universal Surgical Phase LocalizAtion Network, named SurgPLAN++, with the principle of temporal detection. To ensure a global understanding of the surgical procedure, we devise a phase localization strategy for SurgPLAN ++ to predict phase segments across the entire video through phase proposals. For online analysis, to generate high-quality phase proposals, SurgPLAN++ incorporates a data augmentation strategy to extend the streaming video into a pseudo-complete video through mirroring, center-duplication, and down-sampling. For offline analysis, SurgPLAN++ capi-talizes on its global phase prediction framework to continu-ously refine preceding predictions during each online inference step, thereby significantly improving the accuracy of phase recognition. We perform extensive experiments to validate the effectiveness, and our SurgPLAN++ achieves remarkable performance in both online and offline modes, which outper-forms state-of-the-art methods. The source code is available at https://github.com/franciszchenlSurgPLAN-Plus.

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AAAI Conference 2025 Conference Paper

Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment

Yuanfan Zheng
Jinlin Wu
Wuyang Li
Zhen Chen

Domain Adaptive Object Detection (DAOD) transfers knowledge from a labeled source domain to an unannotated target domain under closed-set assumption. Universal DAOD (UniDAOD) extends DAOD to handle open-set, partial-set, and closed-set domain adaptation. In this paper, we first unveil two issues: domain-private category alignment is crucial for global-level features, and the domain probability heterogeneity of features across different levels. To address these issues, we propose a novel Dual Probabilistic Alignment (DPA) framework to model domain probability as Gaussian distribution, enabling the heterogeneity domain distribution sampling and measurement. The DPA consists of three tailored modules: the Global-level Domain Private Alignment (GDPA), the Instance-level Domain Shared Alignment (IDSA), and the Private Class Constraint (PCC). GDPA utilizes the global-level sampling to mine domain-private category samples and calculate alignment weight through a cumulative distribution function to address the global-level private category alignment. IDSA utilizes instance-level sampling to mine domain-shared category samples and calculates alignment weight through Gaussian distribution to conduct the domain-shared category domain alignment to address the feature heterogeneity. The PCC aggregates domain-private category centroids between feature and probability spaces to mitigate negative transfer. Extensive experiments demonstrate that our DPA outperforms state-of-the-art UniDAOD and DAOD methods across various datasets and scenarios, including open, partial, and closed sets.

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IROS Conference 2024 Conference Paper

ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention Understanding

Zhen Chen 0018
Zongming Zhang
Wenwu Guo
Xingjian Luo
Long Bai 0008
Jinlin Wu
Hongliang Ren 0001
Hongbin Liu 0001

Surgical instrument segmentation is crucial in surgical scene understanding, thereby facilitating surgical safety. Existing algorithms directly detected all instruments of predefined categories in the input image, lacking the capability to segment specific instruments according to the surgeon’s intention. During different stages of surgery, surgeons exhibit varying preferences and focus toward different surgical instruments. Therefore, an instrument segmentation algorithm that adheres to the surgeon’s intention can minimize distractions from irrelevant instruments and assist surgeons to a great extent. The recent Segment Anything Model (SAM) reveals the capability to segment objects following prompts, but the manual annotations for prompts are impractical during the surgery. To address these limitations in operating rooms, we propose an audio-driven surgical instrument segmentation framework, named ASI-Seg, to accurately segment the required surgical instruments by parsing the audio commands of surgeons. Specifically, we propose an intention-oriented multimodal fusion to interpret the segmentation intention from audio commands and retrieve relevant instrument details to facilitate segmentation. Moreover, to guide our ASI-Seg segment of the required surgical instruments, we devise a contrastive learning prompt encoder to effectively distinguish the required instruments from the irrelevant ones. Therefore, our ASI-Seg promotes the workflow in the operating rooms, thereby providing targeted support and reducing the cognitive load on surgeons. Extensive experiments are performed to validate the ASI-Seg framework, which reveals remarkable advantages over classical state-of-the-art and medical SAMs in both semantic segmentation and intention-oriented segmentation. The source code is available at https://github.com/Zonmgin-Zhang/ASI-Seg.

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AAAI Conference 2024 Conference Paper

Compositional Inversion for Stable Diffusion Models

Xulu Zhang
Xiao-Yong Wei
Jinlin Wu
Tianyi Zhang
Zhaoxiang Zhang
Zhen Lei
Qing Li

Inversion methods, such as Textual Inversion, generate personalized images by incorporating concepts of interest provided by user images. However, existing methods often suffer from overfitting issues, where the dominant presence of inverted concepts leads to the absence of other desired concepts. It stems from the fact that during inversion, the irrelevant semantics in the user images are also encoded, forcing the inverted concepts to occupy locations far from the core distribution in the embedding space. To address this issue, we propose a method that guides the inversion process towards the core distribution for compositional embeddings. Additionally, we introduce a spatial regularization approach to balance the attention on the concepts being composed. Our method is designed as a post-training approach and can be seamlessly integrated with other inversion methods. Experimental results demonstrate the effectiveness of our proposed approach in mitigating the overfitting problem and generating more diverse and balanced compositions of concepts in the synthesized images. The source code is available at https://github.com/zhangxulu1996/Compositional-Inversion.

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