113 citations · 454 across the 27 of their papers we have counts for
27 papers
DLAM: Distributional Latent Actions with Temporal Constraints
Zuojin Tang, Feifan Luo, Haoyun Liu +10
Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of physical change. Latent action…
ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
Zuojin Tang, Haoyun Liu, Xinyuan Chang +11
Vision-language-action (VLA) models remain constrained by the scarcity of action-labeled robot data, whereas action-free videos provide abundant evidence of how the physical world…
One Token Per Frame: Reconsidering Visual Bandwidth in World Models for VLA Policy
Zuojin Tang, Shengchao Yuan, Xiaoxin Bai +4
Vision-language-action (VLA) models can use visual prediction to anticipate future states, but dense visual features make the generative sequence grow with the number of camera vie…
VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making
Zuojin Tang, Bin Hu, Chenyang Zhao +3
Recent large pretrained models such as LLMs (e.g., GPT series) and VLAs (e.g., OpenVLA) have achieved notable progress on multimodal tasks, yet they are built upon a multi-input si…
Class Incremental Learning via Likelihood Ratio Based Task Prediction
Haowei Lin, Yijia Shao, Weinan Qian +3
Class incremental learning (CIL) is a challenging setting of continual learning, which learns a series of tasks sequentially. Each task consists of a set of unique classes. The key…
Dealing with Cross-Task Class Discrimination in Online Continual Learning
Yiduo Guo, Bing Liu, Dongyan Zhao
Existing continual learning (CL) research regards catastrophic forgetting (CF) as almost the only challenge. This paper argues for another challenge in class-incremental learning (…