Picture for Jinxi Xiang

Jinxi Xiang

Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach

Add code
Oct 20, 2023
Figure 1 for Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Figure 2 for Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Figure 3 for Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Figure 4 for Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Viaarxiv icon

Effortless Cross-Platform Video Codec: A Codebook-Based Method

Add code
Oct 16, 2023
Viaarxiv icon

Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information

Add code
Sep 20, 2023
Figure 1 for Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
Figure 2 for Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
Figure 3 for Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
Figure 4 for Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
Viaarxiv icon

Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression

Add code
Aug 15, 2023
Figure 1 for Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
Figure 2 for Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
Figure 3 for Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
Figure 4 for Dynamic Low-Rank Instance Adaptation for Universal Neural Image Compression
Viaarxiv icon

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Add code
Mar 14, 2023
Figure 1 for CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Figure 2 for CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Figure 3 for CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Figure 4 for CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Viaarxiv icon

Federated contrastive learning models for prostate cancer diagnosis and Gleason grading

Add code
Feb 17, 2023
Figure 1 for Federated contrastive learning models for prostate cancer diagnosis and Gleason grading
Figure 2 for Federated contrastive learning models for prostate cancer diagnosis and Gleason grading
Figure 3 for Federated contrastive learning models for prostate cancer diagnosis and Gleason grading
Figure 4 for Federated contrastive learning models for prostate cancer diagnosis and Gleason grading
Viaarxiv icon

Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study

Add code
Jan 12, 2023
Figure 1 for Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
Figure 2 for Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
Figure 3 for Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
Figure 4 for Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
Viaarxiv icon

Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning

Add code
Apr 07, 2022
Figure 1 for Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning
Figure 2 for Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning
Figure 3 for Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning
Figure 4 for Pan-cancer computational histopathology reveals tumor mutational burden status through weakly-supervised deep learning
Viaarxiv icon

Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation

Add code
Jun 10, 2021
Figure 1 for Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
Figure 2 for Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
Figure 3 for Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
Figure 4 for Self-Ensembling Contrastive Learning for Semi-Supervised Medical Image Segmentation
Viaarxiv icon

MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography

Add code
May 26, 2021
Figure 1 for MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography
Figure 2 for MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography
Figure 3 for MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography
Figure 4 for MMV-Net: A Multiple Measurement Vector Network for Multi-frequency Electrical Impedance Tomography
Viaarxiv icon