activity
20182026
most citedSemantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment

84 citations · 155 across the 28 of their papers we have counts for

collaborators

29 papers

cs.RO2026

AECNav: Active Evidence Consolidation for Efficient Zero-Shot Open-Vocabulary Object Navigation

Guanlin Liu, Shaobin Ling, Renyuan Liu +2

Zero-shot object-goal navigation (ZSON) in open-vocabulary scenarios is challenging, as it requires a robot to locate an arbitrarily specified object in an unseen environment witho…

cs.RO2026

DexTele: A Dual-Arm Dexterous Teleoperation System Based on Motion Retargeting and Adaptive Force Control

Yuanchuan Lai, Qing Gao, Ziyan Liang +3

In dual-arm dexterous teleoperation, cross-platform generalization of motion retargeting and interactivity of grasping are crucial. However, the heterogeneity of robotic architectu…

cs.RO2026

ObjRetarget: An Object-Aware Motion Retargeting Framework with Anthropomorphic Arm Constraints and Polyhedral Hand Modeling

Yuanchuan Lai, Qing Gao, Ziyan Liang +2

Learning robot dexterous manipulation from human manipulation videos requires reliably retargeting human intent to executable robot actions while maintaining stable hand-object con…

cs.CV2025

PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching

Yun Wang, Junjie Hu, Qiaole Dong +4

Temporally consistent depth estimation from stereo video is critical for real-world applications such as augmented reality, where inconsistent depth estimation disrupts the immersi…

cs.CV2025

RoSe: Robust Self-supervised Stereo Matching under Adverse Weather Conditions

Yun Wang, Junjie Hu, Junhui Hou +3

Recent self-supervised stereo matching methods have made significant progress, but their performance significantly degrades under adverse weather conditions such as night, rain, an…

cs.AI2025

ELHPlan: Efficient Long-Horizon Task Planning for Multi-Agent Collaboration

Shaobin Ling, Yun Wang, Chenyou Fan +2

Large Language Models (LLMs) enable intelligent multi-robot collaboration but face fundamental trade-offs: open-loop methods that compile tasks into formal representations for exte…

cs.CV2025

Two-Stage Framework for Efficient UAV-Based Wildfire Video Analysis with Adaptive Compression and Fire Source Detection

Yanbing Bai, Rui-Yang Ju, Lemeng Zhao +4

Unmanned Aerial Vehicles (UAVs) have become increasingly important in disaster emergency response by facilitating aerial video analysis. Due to the limited computational resources…

cs.CV2025

Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts

Yun Wang, Longguang Wang, Chenghao Zhang +6

Recently, learning-based stereo matching networks have advanced significantly. However, they often lack robustness and struggle to achieve impressive cross-domain performance due t…

cs.CV2025

SGFormer: Satellite-Ground Fusion for 3D Semantic Scene Completion

Xiyue Guo, Jiarui Hu, Junjie Hu +2

Recently, camera-based solutions have been extensively explored for scene semantic completion (SSC). Despite their success in visible areas, existing methods struggle to capture co…

cs.CV2023★ 1 cited

Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks

Mingjian Liang, Junjie Hu, Chenyu Bao +3

Recently, RGB-Thermal based perception has shown significant advances. Thermal information provides useful clues when visual cameras suffer from poor lighting conditions, such as l…

cs.RO2023

Descriptor Distillation for Efficient Multi-Robot SLAM

Xiyue Guo, Junjie Hu, Hujun Bao +1

Performing accurate localization while maintaining the low-level communication bandwidth is an essential challenge of multi-robot simultaneous localization and mapping (MR-SLAM). I…

cs.CV2023★ 1 cited

Peer Learning for Unbiased Scene Graph Generation

Liguang Zhou, Junjie Hu, Yuhongze Zhou +2

Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image. To address this, we pro…

cs.CV2023★ 1 cited

Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation

Junjie Hu, Chenyou Fan, Liguang Zhou +3

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelo…

cs.CV2022★ 1 cited

Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification

Liguang Zhou, Yuhongze Zhou, Xiaonan Qi +3

Audio-Visual scene understanding is a challenging problem due to the unstructured spatial-temporal relations that exist in the audio signals and spatial layouts of different object…

cs.RO2022

MultiRoboLearn: An open-source Framework for Multi-robot Deep Reinforcement Learning

Junfeng Chen, Fuqin Deng, Yuan Gao +4

It is well known that it is difficult to have a reliable and robust framework to link multi-agent deep reinforcement learning algorithms with practical multi-robot applications. To…

cs.CV2022★ 3 cited

Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer

Junjie Hu, Chenyou Fan, Mete Ozay +3

We study a practical yet hasn't been explored problem: how a drone can perceive in an environment from different flight heights. Unlike autonomous driving, where the perception is…

cs.CV2022

Dense Depth Distillation with Out-of-Distribution Simulated Images

Junjie Hu, Chenyou Fan, Mete Ozay +2

We study data-free knowledge distillation (KD) for monocular depth estimation (MDE), which learns a lightweight model for real-world depth perception tasks by compressing it from a…

cs.SD2022★ 6 cited

Feature Pyramid Attention based Residual Neural Network for Environmental Sound Classification

Liguang Zhou, Yuhongze Zhou, Xiaonan Qi +3

Environmental sound classification (ESC) is a challenging problem due to the unstructured spatial-temporal relations that exist in the sound signals. Recently, many studies have fo…

cs.CV2022

Deep Depth Completion from Extremely Sparse Data: A Survey

Junjie Hu, Chenyu Bao, Mete Ozay +4

Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applicatio…

cs.CV2021★ 6 cited

FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation

Fuqin Deng, Hua Feng, Mingjian Liang +7

The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises…

cs.CV2021★ 1 cited

PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation

Hualie Jiang, Laiyan Ding, Junjie Hu +1

Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions. Fortunately, the indoor scenes are full…

cs.RO2021

Meta Reinforcement Learning Based Sensor Scanning in 3D Uncertain Environments for Heterogeneous Multi-Robot Systems

Junfeng Chen, Yuan Gao, Junjie Hu +2

We study a novel problem that tackles learning based sensor scanning in 3D and uncertain environments with heterogeneous multi-robot systems. Our motivation is two-fold: first, 3D…

cs.LG2021

Learn2Agree: Fitting with Multiple Annotators without Objective Ground Truth

Chongyang Wang, Yuan Gao, Chenyou Fan +4

The annotation of domain experts is important for some medical applications where the objective ground truth is ambiguous to define, e.g., the rehabilitation for some chronic disea…

cs.CV2021★ 9 cited

Boosting Light-Weight Depth Estimation Via Knowledge Distillation

Junjie Hu, Chenyou Fan, Hualie Jiang +4

Monocular depth estimation (MDE) methods are often either too computationally expensive or not accurate enough due to the trade-off between model complexity and inference performan…