2 citations · 5 across the 12 of their papers we have counts for
8 papers · 1 filter
A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice
Yaowei Bai, Ruiheng Zhang, Yu Lei +20
A global shortage of radiologists has been exacerbated by the significant volume of chest X-ray workloads, particularly in primary care. Although multimodal large language models s…
DriveLaW:Unifying Planning and Video Generation in a Latent Driving World
Tianze Xia, Yongkang Li, Lijun Zhou +9
World models have become crucial for autonomous driving, as they learn how scenarios evolve over time to address the long-tail challenges of the real world. However, current approa…
DiffusionVL: Translating Any Autoregressive Models into Diffusion Vision Language Models
Lunbin Zeng, Jingfeng Yao, Bencheng Liao +3
Diffusion-based decoding has recently emerged as an appealing alternative to autoregressive (AR) generation, offering the potential to update multiple tokens in parallel and reduce…
Towards Scalable Pre-training of Visual Tokenizers for Generation
Jingfeng Yao, Yuda Song, Yucong Zhou +1
The quality of the latent space in visual tokenizers (e.g., VAEs) is crucial for modern generative models. However, the standard reconstruction-based training paradigm produces a l…
Visual Generation Tuning
Jiahao Guo, Sinan Du, Jingfeng Yao +7
Large Vision Language Models (VLMs) effectively bridge the modality gap through extensive pretraining, acquiring sophisticated visual representations aligned with language. However…
MobileI2V: Fast and High-Resolution Image-to-Video on Mobile Devices
Shuai Zhang, Bao Tang, Siyuan Yu +7
Recently, video generation has witnessed rapid advancements, drawing increasing attention to image-to-video (I2V) synthesis on mobile devices. However, the substantial computationa…
Turbo-VAED: Fast and Stable Transfer of Video-VAEs to Mobile Devices
Ya Zou, Jingfeng Yao, Siyuan Yu +3
There is a growing demand for deploying large generative AI models on mobile devices. For recent popular video generative models, however, the Variational AutoEncoder (VAE) represe…
Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models
Jingfeng Yao, Bin Yang, Xinggang Wang
Latent diffusion models with Transformer architectures excel at generating high-fidelity images. However, recent studies reveal an optimization dilemma in this two-stage design: wh…