5 citations · 6 across the 7 of their papers we have counts for
9 papers
ImageTTS: Repurposing Pretrained Image Diffusion Transformers for Speech Synthesis
Hanchen Pei, Shujie Liu, Yayue Deng +4
Pretrained image-generation models have been successfully repurposed for various downstream visual tasks. Whether their learned representations and generative priors can transfer b…
InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation
Yang Yue, Fangyun Wei, Tianyu He +10
Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on d…
Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction
Jinxiao Zhang, Runmin Dong, Xiyong Wu +14
Earth observation is becoming one of the largest data-producing activities in science, yet current pipelines still treat compression as a storage and transmission tool rather than…
Fast Autoregressive Models for Continuous Latent Generation
Tiankai Hang, Jianmin Bao, Fangyun Wei +1
Autoregressive models have demonstrated remarkable success in sequential data generation, particularly in NLP, but their extension to continuous-domain image generation presents si…
UniGraspTransformer: Simplified Policy Distillation for Scalable Dexterous Robotic Grasping
Wenbo Wang, Fangyun Wei, Lei Zhou +9
We introduce UniGraspTransformer, a universal Transformer-based network for dexterous robotic grasping that simplifies training while enhancing scalability and performance. Unlike…
CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation
Qixiu Li, Yaobo Liang, Zeyu Wang +15
The advancement of large Vision-Language-Action (VLA) models has significantly improved robotic manipulation in terms of language-guided task execution and generalization to unseen…
Improved Noise Schedule for Diffusion Training
Tiankai Hang, Shuyang Gu, Xin Geng +1
Diffusion models have emerged as the de facto choice for generating high-quality visual signals across various domains. However, training a single model to predict noise across var…
Scaling the Codebook Size of VQGAN to 100,000 with a Utilization Rate of 99%
Lei Zhu, Fangyun Wei, Yanye Lu +1
In the realm of image quantization exemplified by VQGAN, the process encodes images into discrete tokens drawn from a codebook with a predefined size. Recent advancements, particul…
Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
Lei Zhu, Fangyun Wei, Yanye Lu
In this work, we investigate the potential of a large language model (LLM) to directly comprehend visual signals without the necessity of fine-tuning on multi-modal datasets. The f…