29 citations · 92 across the 24 of their papers we have counts for
25 papers
GaussianSeed: Hierarchical Gaussian Seeding for High-Resolution 3D Occupancy Prediction
Xinzhuo Li, Xianghui Pan, Jiayuan Du +4
Vision-centric 3D occupancy prediction provides dense scene representations essential for autonomous driving and robotic navigation, yet existing methods struggle to scale to high…
MemPose: Category-level Object Pose Estimation with Memory
Xiao Lin, Minghao Zhu, Yun Peng +4
In the pursuit of robust and generalizable category-level object pose estimation, most existing methods adopt parametric formulations that learn effective representations from data…
Kinematics-Aware Multi-Policy Reinforcement Learning for Force-Capable Humanoid Loco-Manipulation
Kaiyan Xiao, Zihan Xu, Cheng Zhe +2
Humanoid robots, with their human-like morphology, hold great potential for industrial applications. However, existing loco-manipulation methods primarily focus on dexterous manipu…
Realizing Text-Driven Motion Generation on NAO Robot: A Reinforcement Learning-Optimized Control Pipeline
Zihan Xu, Mengxian Hu, Kaiyan Xiao +3
Human motion retargeting for humanoid robots, transferring human motion data to robots for imitation, presents significant challenges but offers considerable potential for real-wor…
MoTE: Reconciling Generalization with Specialization for Visual-Language to Video Knowledge Transfer
Minghao Zhu, Zhengpu Wang, Mengxian Hu +5
Transferring visual-language knowledge from large-scale foundation models for video recognition has proved to be effective. To bridge the domain gap, additional parametric modules…
SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection
Yun Peng, Xiao Lin, Nachuan Ma +4
Visual anomaly detection is vital in real-world applications, such as industrial defect detection and medical diagnosis. However, most existing methods focus on local structural an…
Efficient Text-driven Motion Generation via Latent Consistency Training
Mengxian Hu, Minghao Zhu, Xun Zhou +4
Text-driven human motion generation based on diffusion strategies establishes a reliable foundation for multimodal applications in human-computer interactions. However, existing ad…
Causality-based Cross-Modal Representation Learning for Vision-and-Language Navigation
Liuyi Wang, Zongtao He, Ronghao Dang +3
Vision-and-Language Navigation (VLN) has gained significant research interest in recent years due to its potential applications in real-world scenarios. However, existing VLN metho…
Fast and Efficient Local Search for Genetic Programming Based Loss Function Learning
Christian Raymond, Qi Chen, Bing Xue +1
In this paper, we develop upon the topic of loss function learning, an emergent meta-learning paradigm that aims to learn loss functions that significantly improve the performance…
CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge
Xiao Lin, Minghao Zhu, Ronghao Dang +5
Most of existing category-level object pose estimation methods devote to learning the object category information from point cloud modality. However, the scale of 3D datasets is li…
TransPose: 6D Object Pose Estimation with Geometry-Aware Transformer
Xiao Lin, Deming Wang, Guangliang Zhou +2
Estimating the 6D object pose is an essential task in many applications. Due to the lack of depth information, existing RGB-based methods are sensitive to occlusion and illuminatio…
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
Ronghao Dang, Jiangyan Feng, Haodong Zhang +8
We propose InstructDET, a data-centric method for referring object detection (ROD) that localizes target objects based on user instructions. While deriving from referring expressio…
Adaptive Denoising-Enhanced LiDAR Odometry for Degeneration Resilience in Diverse Terrains
Mazeyu Ji, Wenbo Shi, Yujie Cui +2
The flexibility of Simultaneous Localization and Mapping (SLAM) algorithms in various environments has consistently been a significant challenge. To address the issue of LiDAR odom…
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation Learning
Minghao Zhu, Xiao Lin, Ronghao Dang +2
As the most essential property in a video, motion information is critical to a robust and generalized video representation. To inject motion dynamics, recent works have adopted fra…
A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation
Liuyi Wang, Zongtao He, Jiagui Tang +4
Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancem…
PASTS: Progress-Aware Spatio-Temporal Transformer Speaker For Vision-and-Language Navigation
Liuyi Wang, Chengju Liu, Zongtao He +4
Vision-and-language navigation (VLN) is a crucial but challenging cross-modal navigation task. One powerful technique to enhance the generalization performance in VLN is the use of…
MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation
Zongtao He, Liuyi Wang, Shu Li +3
Vision-and-Language Navigation (VLN) aims to develop intelligent agents to navigate in unseen environments only through language and vision supervision. In the recently proposed co…
Multiple Thinking Achieving Meta-Ability Decoupling for Object Navigation
Ronghao Dang, Lu Chen, Liuyi Wang +3
We propose a meta-ability decoupling (MAD) paradigm, which brings together various object navigation methods in an architecture system, allowing them to mutually enhance each other…
Meta-Learning Adaptive Loss Functions
Christian Raymond, Qi Chen, Bing Xue +1
Loss function learning is a new meta-learning paradigm that aims to automate the essential task of designing a loss function for a machine learning model. Existing techniques for l…
Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning
Christian Raymond, Qi Chen, Bing Xue +1
In this paper, we develop upon the emerging topic of loss function learning, which aims to learn loss functions that significantly improve the performance of the models trained und…
Search for or Navigate to? Dual Adaptive Thinking for Object Navigation
Ronghao Dang, Liuyi Wang, Zongtao He +3
"Search for" or "Navigate to"? When finding an object, the two choices always come up in our subconscious mind. Before seeing the target, we search for the target based on experien…
Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment
Xin Li, Daqi Zhu, Bing Sun +3
In this paper, formation tracking for a multi-AUV system (MAS) using an improved adaptive sliding mode control method is studied in the Three Dimensional (3-D) underwater environme…
Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification
Wei Ye, Hao Tian, Qijun Chen
Graph kernels are conventional methods for computing graph similarities. However, the existing R-convolution graph kernels cannot resolve both of the two challenges: 1) Comparing g…
Unbiased Directed Object Attention Graph for Object Navigation
Ronghao Dang, Zhuofan Shi, Liuyi Wang +3
Object navigation tasks require agents to locate specific objects in unknown environments based on visual information. Previously, graph convolutions were used to implicitly explor…