5 citations · 5 across the 4 of their papers we have counts for
5 papers
URDF-Anything: Constructing Articulated Objects with 3D Multimodal Language Model
Zhe Li, Xiang Bai, Jieyu Zhang +5
Constructing accurate digital twins of articulated objects is essential for robotic simulation training and embodied AI world model building, yet historically requires painstaking…
H2R: A Human-to-Robot Data Augmentation for Robot Pre-training from Videos
Guangrun Li, Yaoxu Lyu, Zhuoyang Liu +3
Large-scale pre-training using egocentric human videos has proven effective for robot learning. However, the models pre-trained on such data can be suboptimal for robot learning du…
BEV-TSR: Text-Scene Retrieval in BEV Space for Autonomous Driving
Tao Tang, Dafeng Wei, Zhengyu Jia +12
The rapid development of the autonomous driving industry has led to a significant accumulation of autonomous driving data. Consequently, there comes a growing demand for retrieving…
When to Learn What: Model-Adaptive Data Augmentation Curriculum
Chengkai Hou, Jieyu Zhang, Tianyi Zhou
Data augmentation (DA) is widely used to improve the generalization of neural networks by enforcing the invariances and symmetries to pre-defined transformations applied to input d…
Subclass-balancing Contrastive Learning for Long-tailed Recognition
Chengkai Hou, Jieyu Zhang, Haonan Wang +1
Long-tailed recognition with imbalanced class distribution naturally emerges in practical machine learning applications. Existing methods such as data reweighing, resampling, and s…