119 citations · 163 across the 11 of their papers we have counts for
12 papers
Boosting Consistency in Story Visualization with Rich-Contextual Conditional Diffusion Models
Fei Shen, Hu Ye, Sibo Liu +4
Recent research showcases the considerable potential of conditional diffusion models for generating consistent stories. However, current methods, which predominantly generate stori…
V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation
Cong Wang, Kuan Tian, Jun Zhang +7
In the field of portrait video generation, the use of single images to generate portrait videos has become increasingly prevalent. A common approach involves leveraging generative…
Ensembling Diffusion Models via Adaptive Feature Aggregation
Cong Wang, Kuan Tian, Yonghang Guan +4
The success of the text-guided diffusion model has inspired the development and release of numerous powerful diffusion models within the open-source community. These models are typ…
Effortless Cross-Platform Video Codec: A Codebook-Based Method
Kuan Tian, Yonghang Guan, Jinxi Xiang +3
Under certain circumstances, advanced neural video codecs can surpass the most complex traditional codecs in their rate-distortion (RD) performance. One of the main reasons for the…
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Feng Luo, Jinxi Xiang, Jun Zhang +2
The recent use of diffusion prior, enhanced by pre-trained text-image models, has markedly elevated the performance of image super-resolution (SR). To alleviate the huge computatio…
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models
Fei Shen, Hu Ye, Jun Zhang +3
Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and ta…
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
Kuan Tian, Yonghang Guan, Jinxi Xiang +3
The state-of-the-art neural video codecs have outperformed the most sophisticated traditional codecs in terms of RD performance in certain cases. However, utilizing them for practi…
Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
Yue Lv, Jinxi Xiang, Jun Zhang +3
The latest advancements in neural image compression show great potential in surpassing the rate-distortion performance of conventional standard codecs. Nevertheless, there exists a…
IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
Hu Ye, Jun Zhang, Sibo Liu +2
Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tr…
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
Siteng Chen, Xiyue Wang, Jun Zhang +8
Background: Clear cell renal cell carcinoma (ccRCC) is the most common renal-related tumor with high heterogeneity. There is still an urgent need for novel diagnostic and prognosti…
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning
Boxuan Zhao, Jun Zhang, Deheng Ye +4
Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic rele…
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
Siteng Chen, Jinxi Xiang, Xiyue Wang +6
Tumor mutational burden (TMB) is a potential genomic biomarker of immunotherapy. However, TMB detected through whole exome sequencing lacks clinical penetration in low-resource set…