14 citations · 19 across the 8 of their papers we have counts for
8 papers
What Matters for Latent Actions in Robot Learning
Xizhou Bu, Qingda Hu, Lei Zhou +13
Latent Action Models (LAMs) have emerged as a promising paradigm for enabling robot learning to leverage large-scale unlabeled videos through latent actions that serve as compact s…
LAFP: Preserving Latent Action Structure in Latent Policy Learning via Flow Matching
Jiexi Lyu, Xizhou Bu, Qingqiu Huang +4
Learning high-quality latent actions from large-scale unlabeled videos, coupled with limited real-world interaction data for training an action decoder, has emerged as a promising…
Data-Asymmetric Latent Imagination and Reranking for 3D Robotic Imitation Learning
Lianghao Luo, Xizhou Bu, Ruyan Liu +5
Robotic imitation learning typically assumes access to optimal demonstrations, yet real-world data collection often yields suboptimal, exploratory, or even failed trajectories. Dis…
DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
Yingyan Li, Shuyao Shang, Weisong Liu +10
Scaling Vision-Language-Action (VLA) models on large-scale data offers a promising path to achieving a more generalized driving intelligence. However, VLA models are limited by a `…
Active Pointly-Supervised Instance Segmentation
Chufeng Tang, Lingxi Xie, Gang Zhang +3
The requirement of expensive annotations is a major burden for training a well-performed instance segmentation model. In this paper, we present an economic active learning setting,…
Visual Recognition by Request
Chufeng Tang, Lingxi Xie, Xiaopeng Zhang +2
Humans have the ability of recognizing visual semantics in an unlimited granularity, but existing visual recognition algorithms cannot achieve this goal. In this paper, we establis…
Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation
Chufeng Tang, Hang Chen, Xiao Li +3
Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to t…
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization
Chufeng Tang, Lu Sheng, Zhaoxiang Zhang +1
Pedestrian attribute recognition has been an emerging research topic in the area of video surveillance. To predict the existence of a particular attribute, it is demanded to locali…