most citedAWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation

2 citations · 3 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2025

CoMo: Learning Continuous Latent Motion from Internet Videos for Scalable Robot Learning

Jiange Yang, Yansong Shi, Haoyi Zhu +6

Unsupervised learning of latent motion from Internet videos is crucial for robot learning. Existing discrete methods generally mitigate the shortcut learning caused by extracting e…

cs.RO2024

Tra-MoE: Learning Trajectory Prediction Model from Multiple Domains for Adaptive Policy Conditioning

Jiange Yang, Haoyi Zhu, Yating Wang +3

Learning from multiple domains is a primary factor that influences the generalization of a single unified robot system. In this paper, we aim to learn the trajectory prediction mod…

cs.CV2024

SPA: 3D Spatial-Awareness Enables Effective Embodied Representation

Haoyi Zhu, Honghui Yang, Yating Wang +3

In this paper, we introduce SPA, a novel representation learning framework that emphasizes the importance of 3D spatial awareness in embodied AI. Our approach leverages differentia…

cs.CV20241 cited

Efficient Test-Time Prompt Tuning for Vision-Language Models

Yuhan Zhu, Guozhen Zhang, Chen Xu +4

Vision-language models have showcased impressive zero-shot classification capabilities when equipped with suitable text prompts. Previous studies have shown the effectiveness of te…

cs.CV20242 cited

AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportation

Yuhan Zhu, Yuyang Ji, Zhiyu Zhao +2

Pre-trained vision-language models (VLMs) have shown impressive results in various visual classification tasks. However, we often fail to fully unleash their potential when adaptin…