Author name cluster

Hongbo Chen

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6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Generalizing Vision-Language Models with Dedicated Prompt Guidance

Xinyao Li
Yinjie Min
Hongbo Chen
Zhekai Du
Fengling Li
Jingjing Li

Fine-tuning large pretrained vision-language models (VLMs) has emerged as a prevalent paradigm for downstream adaptation, yet it faces a critical trade-off between domain specificity and domain generalization (DG) ability. Current methods typically fine-tune a universal model on the entire dataset, which potentially compromises the ability to generalize to unseen domains. To fill this gap, we provide a theoretical understanding of the generalization ability for VLM fine-tuning, which reveals that training multiple parameter-efficient expert models on partitioned source domains leads to better generalization than fine-tuning a universal model. Inspired by this finding, we propose a two-step domain-expert-Guided DG (GuiDG) framework. GuiDG first employs prompt tuning to obtain source domain experts, then introduces a Cross-Modal Attention module to guide the fine-tuning of the vision encoder via adaptive expert integration. To better evaluate few-shot DG, we construct ImageNet-DG from ImageNet and its variants. Extensive experiments on standard DG benchmarks and ImageNet-DG demonstrate that GuiDG improves upon state-of-the-art fine-tuning methods while maintaining efficiency.

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AAMAS Conference 2025 Conference Paper

Lite-DIO Is Actually What You Need for Efficient Inertial Localization

Yan Li
Meng Liu
Zhongchen Shi
Yanqing Hou
Liang Xie
Hongbo Chen
Erwei Yin

In this work, we propose a simple and effective framework (i. e. , Lite- DIO), marking the first attempt to accelerate deep inertial odometry with knowledge distillation. In Lite-DIO, we first independently construct the Transformer-based teacher model and a lightweight student network. Then, adaptive transferring knowledge is enabled between the teacher model and the student network in a duallevel contrastive distillation manner. With such design, the distilled knowledge comes from not only the teacher model’s predictions but also the latent high-order collaborative semantics preserved in embeddings. Extensive experiments conducted on three real-world datasets demonstrate that the proposed Lite-DIO significantly reduces model size and inference time compared to existing popular alternatives, while the compressed model still maintains competitive localization accuracy.

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IROS Conference 2025 Conference Paper

RAVES-Calib: Robust, Accurate and Versatile Extrinsic Self Calibration Using Optimal Geometric Features

Haoxin Zhang
Shuaixin Li
Xiaozhou Zhu
Xiao Zhang
Hongbo Chen
Wen Yao

In this paper, we present a user-friendly LiDAR-camera calibration toolkit that is compatible with various LiDAR and camera sensors and requires only a single pair of laser points and a camera image in targetless environments. Our approach eliminates the need for an initial transform and remains robust even with large positional and rotational LiDAR-camera extrinsic parameters. We employ the Gluestick pipeline to establish 2D-3D point and line feature correspondences for a robust and automatic initial guess. To enhance accuracy, we quantitatively analyze the impact of feature distribution on calibration results and adaptively weight the cost of each feature based on these metrics. As a result, extrinsic parameters are optimized by filtering out the adverse effects of inferior features. We validated our method through extensive experiments across various LiDAR-camera sensors in both indoor and outdoor settings. The results demonstrate that our method provides superior robustness and accuracy compared to SOTA techniques. Our code is open-sourced on GitHub 1 to benefit the community.

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ICRA Conference 2024 Conference Paper

CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms

Shipeng Zhong
Hongbo Chen
Yuhua Qi
Dapeng Feng
Zhiqiang Chen
Jin Wu 0002
Weisong Wen
Ming Liu 0001

Collaborative state estimation using different heterogeneous sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, posing a significant research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without the need for anchor deployment. The system efficiently distributes computationally intensive tasks to a central server, thereby reducing the computational burden on individual robots for local odometry calculations. The server back-end establishes a global reference by leveraging shared data and refining joint pose graph optimization through place recognition, global optimization techniques, and removal of outlier data to ensure precise and robust collaborative state estimation. Extensive evaluations of our system, utilizing both publicly available datasets and our custom datasets, demonstrate significant enhancements in the accuracy of collaborative SLAM estimates. Moreover, our system exhibits remarkable proficiency in large-scale missions, seamlessly enabling ten robots to collaborate effectively in performing SLAM tasks. In order to contribute to the research community, we will make our code open-source and accessible at https://github.com/PengYu-team/Co-LRIO.

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ICRA Conference 2024 Conference Paper

RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments

Zhiqiang Chen
Hongbo Chen
Yuhua Qi
Shipeng Zhong
Dapeng Feng
Jin Wu 0002
Weisong Wen
Ming Liu 0001

LiDAR-based localization is valuable for applications like mining surveys and underground facility maintenance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenarios. This paper presents RELEAD, a LiDAR-centric solution designed to address scan-matching degradation. Our method enables degeneracy-free point cloud registration by solving constrained ESIKF updates in the front end and incorporates multisensor constraints, even when dealing with outlier measurements, through graph optimization based on Graduated Non-Convexity (GNC). Additionally, we propose a robust Incremental Fixed Lag Smoother (rIFL) for efficient GNC-based optimization. RELEAD has undergone extensive evaluation in degenerate scenarios and has outperformed existing state-of-the-art LiDAR-Inertial odometry and LiDAR-Visual-Inertial odometry methods.

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YNICL Journal 2016 Journal Article

Increased frontal functional networks in adult survivors of childhood brain tumors

Hongbo Chen
Liya Wang
Tricia Z. King
Hui Mao

Childhood brain tumors and associated treatment have been shown to affect brain development and cognitive outcomes. Understanding the functional connectivity of brain many years after diagnosis and treatment may inform the development of interventions to improve the long-term outcomes of adult survivors of childhood brain tumors. This work investigated the frontal region functional connectivity of 16 adult survivors of childhood cerebellar tumors after an average of 14.9 years from diagnosis and 16 demographically-matched controls using resting state functional MRI (rs-fMRI). Independent component analysis (ICA) was applied to identify the resting state activity from rs-fMRI data and to select the specific regions associated with executive functions, followed by the secondary analysis of the functional networks connecting these regions. It was found that survivors exhibited differences in the functional connectivity in executive control network (ECN), default mode network (DMN) and salience network (SN) compared to demographically-matched controls. More specifically, the number of functional connectivity observed in the survivors is higher than that in the controls, and with increased strength, or stronger correlation coefficient between paired seeds, in survivors compared to the controls. Observed hyperconnectivity in the selected frontal functional network thus is consistent with findings in patients with other neurological injuries and diseases.

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