8 papers
Ordered Action Tokens for Visuomotor Policy Learning
Chaoqi Liu, Yue Zhao, Haonan Chen +4
Action tokenization maps continuous robot action chunks to discrete tokens and has become an important interface for modern visuomotor policies. Existing approaches either rely on…
Modality-Aware and Anatomical Vector-Quantized Autoencoding for Multimodal Brain MRI
Mingjie Li, Edward Kim, Yue Zhao +2
Learning a robust Variational Autoencoder (VAE) is a fundamental step for many deep learning applications in medical image analysis, such as MRI synthesizes. Existing brain VAEs pr…
OAT: Ordered Action Tokenization
Chaoqi Liu, Xiaoshen Han, Jiawei Gao +3
Autoregressive policies offer a compelling foundation for scalable robot learning by enabling discrete abstraction, token-level reasoning, and flexible inference. However, applying…
Spherical Leech Quantization for Visual Tokenization and Generation
Yue Zhao, Hanwen Jiang, Zhenlin Xu +3
Non-parametric quantization has received much attention due to its efficiency on parameters and scalability to a large codebook. In this paper, we present a unified formulation of…
VideoPrism: A Foundational Visual Encoder for Video Understanding
Long Zhao, Nitesh B. Gundavarapu, Liangzhe Yuan +16
We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus…
RayZer: A Self-supervised Large View Synthesis Model
Hanwen Jiang, Hao Tan, Peng Wang +8
We present RayZer, a self-supervised multi-view 3D Vision model trained without any 3D supervision, i.e., camera poses and scene geometry, while exhibiting emerging 3D awareness. C…