2 citations · 5 across the 12 of their papers we have counts for
15 papers
Rethinking Representations for World-Action Modeling
Haoyi Jiang, Liu Liu, Xinjiang Wang +12
World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction. We study the design o…
ReWorld: Representation Learning for World Action Models
Tianze Xia, Lijun Zhou, Kaixin Xiong +8
World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only the final outputs, leaving inter…
UniX: Unifying Autoregression and Diffusion for Chest X-Ray Understanding and Generation
Ruiheng Zhang, Jingfeng Yao, Huangxuan Zhao +9
Despite recent progress, medical foundation models still struggle to unify visual understanding and generation, as these tasks have inherently conflicting goals: semantic abstracti…
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…
FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification
Jingfeng Yao, Wang Cheng, Wenyu Liu +1
Diffusion Transformers (DiT) have attracted significant attention in research. However, they suffer from a slow convergence rate. In this paper, we aim to accelerate DiT training w…
LKCell: Efficient Cell Nuclei Instance Segmentation with Large Convolution Kernels
Ziwei Cui, Jingfeng Yao, Lunbin Zeng +3
The segmentation of cell nuclei in tissue images stained with the blood dye hematoxylin and eosin (HE) is essential for various clinical applications and analyses. Due to the c…
EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning
Jingfeng Yao, Xinggang Wang, Yuehao Song +5
The diagnosis and treatment of chest diseases play a crucial role in maintaining human health. X-ray examination has become the most common clinical examination means due to its ef…
ViTGaze: Gaze Following with Interaction Features in Vision Transformers
Yuehao Song, Xinggang Wang, Jingfeng Yao +3
Gaze following aims to interpret human-scene interactions by predicting the person's focal point of gaze. Prevailing approaches often adopt a two-stage framework, whereby multi-mod…