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Yulong Li

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12 papers

2 author rows

AAAI Conference 2026 Conference Paper

DeepGB-TB: A Risk-Balanced Cross-Attention Gradient-Boosted Convolutional Network for Rapid, Interpretable Tuberculosis Screening

Zhixiang Lu
Yulong Li
Feilong Tang
Zhengyong Jiang
Chong Li
Mian Zhou
Tenglong Li
Jionglong Su

Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We propose DeepGB-TB, a non-invasive system that instantly assigns TB risk scores using only cough audio and basic demographic data. The model couples a lightweight one-dimensional convolutional neural network for audio processing with a gradient-boosted decision tree for tabular features. Its principal innovation is a Cross-Modal Bidirectional Cross-Attention module (CM-BCA) that iteratively exchanges salient cues between modalities, emulating the way clinicians integrate symptoms and risk factors. To meet the clinical priority of minimizing missed cases, we design a Tuberculosis Risk-Balanced Loss (TRBL) that places stronger penalties on false-negative predictions, thereby reducing high-risk misclassifications. DeepGB-TB is evaluated on a diverse dataset of 1,105 patients collected across seven countries, achieving an AUROC of 0.903 and an F1-score of 0.851, representing a new state of the art. Its computational efficiency enables real-time, offline inference directly on common mobile devices, making it ideal for low-resource settings. Importantly, the system produces clinically validated explanations that promote trust and adoption by frontline health workers. By coupling AI innovation with public-health requirements for speed, affordability, and reliability, DeepGB-TB offers a tool for advancing global TB control.

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AAAI Conference 2026 Conference Paper

DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning

Xiwei Liu
Yulong Li
Feilong Tang
Imran Razzak

Adapting Large Multimodal Models (LMMs) to real-world scenarios poses the dual challenges of learning from sequential data streams while handling frequent modality incompleteness, a task known as Continual Missing Modality Learning (CMML). However, existing works on CMML have predominantly relied on prompt tuning, a technique that struggles with this task due to cross-task interference between its learnable prompts in their shared embedding space. A naive application of Low-Rank Adaptation (LoRA) with modality-shared module will also suffer modality interference from competing gradients. To this end, we propose DeLo, the first framework to leverage a novel dual-decomposed low-rank expert architecture for CMML. Specifically, this architecture resolves modality interference through decomposed LoRA expert, dynamically composing LoRA update matrix with rank-one factors from disentangled modality-specific factor pools. Embedded within a task-partitioned framework that structurally prevents catastrophic forgetting, this expert system is supported by two key mechanisms: a Cross-Modal Guided Routing strategy to handle incomplete data and a Task-Key Memory for efficient, task-agnostic inference. Extensive experiments on established CMML benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches. This highlights the value of a principled, architecturally-aware LoRA design for real-world multimodal challenges.

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AAAI Conference 2026 Conference Paper

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning

Lejun Ai
Yulong Li
Haodong Yi
Jixuan Xie
Yue Wang
Jia Liu
Min Chen
Rui Wang

Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evalutaed on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.

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NeurIPS Conference 2025 Conference Paper

Decoding Causal Structure: End-to-End Mediation Pathways Inference

Yulong Li
Xiwei Liu
Feilong Tang
Ming Hu
Jionglong Su
Zongyuan Ge
Imran Razzak
Eran Segal

Causal mediation analysis is crucial for deconstructing complex mechanisms of action. However, in current mediation analysis, complex structures derived from causal discovery lack direct interpretation of mediation pathways, while traditional mediation analysis and effect estimation are limited by the reliance on pre-specified pathways, leading to a disconnection between structure discovery and causal mechanism understanding. Therefore, a unified framework integrating structure discovery, pathway identification, and effect estimation systematically quantifies mediation pathways under structural uncertainty, enabling automated identification and inference of mediation pathways. To this end, we propose Structure-Informed Guided Mediation Analysis (SIGMA), which guides automated mediation pathway identification through probabilistic causal structure discovery and uncertainty quantification, enabling end-to-end propagation of structural uncertainty from structure learning to effect estimation. Specifically, SIGMA employs differentiable Flow-Structural Equation Models to learn structural posteriors, generating diverse Directed Acyclic Graphs (DAGs) to quantify structural uncertainty. Based on these DAGs, we introduce the Path Stability Score to evaluate the marginal probability of pathways, identifying high-confidence mediation paths. For identified mediation pathways, we integrate Efficient Influence Functions with Bayesian model averaging to fuse within-structure estimation uncertainty and between-structure effect variation, propagating uncertainty to the final effect estimates. In synthetic data experiments, SIGMA achieves state-of-the-art performance in pathway identification accuracy and effect quantification precision under structures uncertainty, concurrent multiple pathways, and nonlinear scenarios. In real-world applications using Human Phenotype Project data, SIGMA identifies mediation effects of sleep quality on cardiovascular health through inflammatory and metabolic pathways, uncovering previously unspecified multiple mediation paths.

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AAAI Conference 2025 Conference Paper

KD-MSLRT: Lightweight Sign Language Recognition Model Based on Mediapipe and 3D to 1D Knowledge Distillation

Yulong Li
Bolin Ren
Ke Hu
Changyuan Liu
Zhengyong Jiang
Kang Dang
Jionglong Su

Artificial intelligence has achieved notable results in sign language recognition and translation. However, relatively few efforts have been made to significantly improve the quality of life for the 72 million hearing-impaired people worldwide. Sign language translation models, relying on video inputs, involves with large parameter sizes, making it time-consuming and computationally intensive to be deployed. This directly contributes to the scarcity of human-centered technology in this field. Additionally, the lack of datasets in sign language translation hampers research progress in this area. To address these, we first propose a cross-modal multi-knowledge distillation technique from 3D to 1D and a novel end-to-end pre-training text correction framework. Compared to other pre-trained models, our framework achieves significant advancements in correcting text output errors. Our model achieves a decrease in Word Error Rate (WER) of at least 1.4% on PHOENIX14 and PHOENIX14T datasets compared to the state-of-the-art CorrNet. Additionally, the TensorFlow Lite (TFLite) quantized model size is reduced to 12.93 MB, making it the smallest, fastest, and most accurate model to date. We have also collected and released extensive Chinese sign language datasets, and developed a specialized training vocabulary. To address the lack of research on data augmentation for landmark data, we have designed comparative experiments on various augmentation methods. Moreover, we performed a simulated deployment and prediction of our model on Intel platform CPUs and assessed the feasibility of deploying the model on other platforms.

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ECAI Conference 2025 Conference Paper

Measuring Ageism in Large Language Models

Shuo Liu
Jiaoyun Yang
Yulong Li
Hongtu Chen
Ning An 0001

As large language models gain prominence, there is increasing concern about the potential biases they may perpetuate. While various biases have been studied, ageism in language models remains underexplored. According to the World Health Organization, ageism can significantly impact the physical and mental well-being of older adults, an impact that could grow as the global aging population increases. To address this research gap, we developed AgeismSet, a comprehensive Chinese dataset comprising 6, 444 sentences, enhanced with neutral impression options to provide a balanced evaluation framework. Our study then used AgeismSet to investigate ageism in large language models across cognitive, affective, and behavioral dimensions, using AgeismSet to evaluate models such as GPT-4, GPT-3. 5, GLM-3-Turbo, ERNIE Bot, Gemini Pro, and DeepSeek-V3. Our findings, quantified by the Ageism Score (AS), reveal that while some models perform well, there is considerable room for improvement in mitigating ageism. This work underscores the necessity for targeted interventions to ensure more equitable AI systems.

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NeurIPS Conference 2025 Conference Paper

Towards Dynamic 3D Reconstruction of Hand-Instrument Interaction in Ophthalmic Surgery

Ming Hu
Zhengdi Yu
Feilong Tang
Kaiwen Chen
Yulong Li
Imran Razzak
Junjun He
Tolga Birdal

Accurate 3D reconstruction of hands and instruments is critical for vision-based analysis of ophthalmic microsurgery, yet progress has been hampered by the lack of realistic, large-scale datasets and reliable annotation tools. In this work, we introduce OphNet-3D, the first extensive RGB-D dynamic 3D reconstruction dataset for ophthalmic surgery, comprising 41 sequences from 40 surgeons and totaling 7. 1 million frames, with fine-grained annotations of 12 surgical phases, 10 instrument categories, dense MANO hand meshes, and full 6-DoF instrument poses. To scalably produce high-fidelity labels, we design a multi-stage automatic annotation pipeline that integrates multi-view data observation, data-driven motion prior with cross-view geometric consistency and biomechanical constraints, along with a combination of collision-aware interaction constraints for instrument interactions. Building upon OphNet-3D, we establish two challenging benchmarks—bimanual hand pose estimation and hand–instrument interaction reconstruction—and propose two dedicated architectures: H-Net for dual-hand mesh recovery and OH-Net for joint reconstruction of two-hand–two-instrument interactions. These models leverage a novel spatial reasoning module with weak-perspective camera modeling and collision-aware center-based representation. Both architectures outperform existing methods by substantial margins, achieving improvements of over 2mm in Mean Per Joint Position Error (MPJPE) and up to 23\% in ADD-S metrics for hand and instrument reconstruction, respectively.

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EAAI Journal 2024 Journal Article

A differential evolution framework based on the fluid model for feature selection

Min Li
Junke Wang
Rutun Cao
Yulong Li

Feature selection in machine learning is a crucial step to effectively address the issue of feature redundancy in classification problems. Numerous feature selection algorithms have been developed to minimize the number of features, reduce computational cost, and improve classification accuracy. Differential evolution algorithms have the advantage of being simple in structure, robust, fast in convergence, and frequently used to solve feature selection problems. However, it is worth noting that differential evolution algorithms are susceptible to local optimum and stagnation issues, particularly when applied to high-dimensional data. To address this issue, in this study, we propose a differential evolution framework based on the fluid model, named DEF - F M, for feature selection. DEF - F M has the capability to speed up the convergence of differential evolution algorithms and alleviate the effects of local optima. The proposed framework is validated and compared against eight popular differential evolution algorithms using 12 publicly available benchmark datasets and experimental results unequivocally demonstrate the superiority of the proposed framework.

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IROS Conference 2024 Conference Paper

HSS-SLAM: Human-in-the-Loop Semantic SLAM Represented by Superquadrics

Yulong Li
Yunzhou Zhang
Bin Zhao
Zhiyao Zhang
You Shen
Tengda Zhang
Guolu Chen

The advancement of object detection algorithms has catalyzed the development of object-level semantic SLAM. However, due to missed and false detections, object-level semantic SLAM fails to represent the objects within the scene adequately. Therefore, this paper proposes a novel object-level semantic SLAM termed HSS-SLAM. We incorporate human-in-the-loop into our method, establishing an interaction module to facilitate human editing and rectifying semantic information. Additionally, to minimize the manual correction workload, a lightweight and intuitive method for semantic extension is proposed, augmenting the semantic richness of the global map with a few operations. Furthermore, our method adopts superquadrics for object representation, enabling detailed descriptions of various object shapes. This mitigates the limitation of conventional semantic mapping, where objects are difficult to distinguish due to the reliance on a single-shape representation. Subsequently, precise estimation of superquadric parameters and camera poses is achieved through joint optimization. Extensive experiments conducted on TUM RGB-D and Scenes V2 datasets demonstrate that the proposed approach exhibits competitive performance, surpassing current methods in both object representation and camera localization accuracy.

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ICRA Conference 2024 Conference Paper

L-VIWO: Visual-Inertial-Wheel Odometry based on Lane Lines

Bin Zhao
Yunzhou Zhang
Junjie Huang
Xichen Zhang
Zeyu Long
Yulong Li

To achieve precise localization for autonomous vehicles and mitigate the problem of accumulated drift error in odometry, this paper proposes L-VIWO, a Visual-Inertial-Wheel Odometry based on lane lines. This method effectively utilizes the lateral constraints provided by lane lines to eliminate and relieve the incrementally accumulated pose errors. Firstly, we introduce a lane line tracking method that enables multi-frame tracking of the same lane line, thereby obtaining multi-frame data of a lane line. Then, we utilize multi-frame data of the lane lines and the curvature characteristics of adjacent lane lines to optimize the positions of the lane line sample points, thus building a reliable lane line map. Finally, we use the built local lane line map to correct the position of the vehicle. Based on the corrected position and prior pose from the odometry, we build a graph optimization model to optimize the pose of the vehicle. Through localization experiments on the KAIST dataset, it has been demonstrated that the proposed method effectively enhances the localization accuracy of odometry, thus confirming the effectiveness of the method.

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ICRA Conference 2024 Conference Paper

VPE-SLAM: Neural Implicit Voxel-permutohedral Encoding for SLAM

Zhiyao Zhang
Yunzhou Zhang
You Shen
Lei Rong
Sizhan Wang
Xin Ouyang
Yulong Li

NeRF can reconstruct incredibly realistic environmental maps in dense simultaneous localization and mapping, providing robots with more comprehensive scene map information. However, NeRF often struggles with geometric distortions in indoor reconstructions. To correct geometric distortions, we develop VPE-SLAM, based on the proposed voxel-permutohedral encoding, which can incrementally reconstruct maps of unknown scenes. Specifically, voxel-permutohedral encoding combines a sparse voxel feature grid created by an octree and multi-resolution permutohedral tetrahedral feature grids to represent the scene effectively. Especially when dealing with object edges, our method can effectively encode the geometry and texture of edges by the hybrid structural grid. We propose a novel local bundle adjustment module that utilizes a sliding window mechanism to manage adjacent keyframes requiring optimization. Furthermore, the proposed method establishes local map consistency by repeatedly optimizing keyframes that were initially under-optimized through a compensation strategy. The consistency of the local map can enhance the adaptability of our method to challenging scenes. Extensive experiments demonstrate that our method can achieve accurate camera tracking and produce high-quality reconstruction results on the Replica and ScanNet datasets. The source code will be available at https://github.com/NeuCV-IRMI/VPE-SLAM.

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IROS Conference 2022 Conference Paper

Scene Editing as Teleoperation: A Case Study in 6DoF Kit Assembly

Yulong Li
Shubham Agrawal
Jen-Shuo Liu
Steven K. Feiner
Shuran Song

Studies in robot teleoperation have been centered around action specifications-from continuous joint control to discrete end-effector pose control. However, these “robot-centric” interfaces often require skilled operators with extensive robotics expertise. To make teleoperation accessible to nonexpert users, we propose the framework “Scene Editing as Teleoperation” (SEaT), where the key idea is to transform the traditional “robot-centric” interface into a “scene-centric” interface-instead of controlling the robot, users focus on specifying the task's goal by manipulating digital twins of the real-world objects. As a result, a user can perform teleoperation without any expert knowledge of the robot hardware. To achieve this goal, we utilize a category-agnostic scene-completion algorithm that translates the real-world workspace (with unknown objects) into a manipulable virtual scene representation and an action-snapping algorithm that refines the user input before generating the robot's action plan. To train the algorithms, we procedurely generated a large-scale, diverse kit-assembly dataset that contains object-kit pairs that mimic real-world object-kitting tasks. Our experiments in simulation and on a real-world system demonstrate that our framework improves both the efficiency and success rate for 6DoF kit-assembly tasks. A user study demonstrates that SEaT framework participants achieve a higher task success rate and report a lower subjective workload compared to an alternative robot-centric interface.

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