ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning

no code implementations • 21 May 2025 • Ziqiang Xu, Qi Dai, Tian Xie, Yifan Yang, Kai Qiu, Dongdong Chen, Zuxuan Wu, Chong Luo

Video understanding is inherently intention-driven-humans naturally focus on relevant frames based on their goals.

Pseudo Label Reinforcement Learning (RL) +1

Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation

1 code implementation • 9 Mar 2025 • Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, Jingbo Zhu

The field of neural machine translation (NMT) has changed with the advent of large language models (LLMs).

Decoder Machine Translation +2

Towards Universal Learning-based Model for Cardiac Image Reconstruction: Summary of the CMRxRecon2024 Challenge

1 code implementation • 5 Mar 2025 • Fanwen Wang, Zi Wang, Yan Li, Jun Lyu, Chen Qin, Shuo Wang, Kunyuan Guo, Mengting Sun, Mingkai Huang, Haoyu Zhang, Michael Tänzer, Qirong Li, Xinran Chen, Jiahao Huang, Yinzhe Wu, Kian Anvari Hamedani, Yuntong Lyu, Longyu Sun, Qing Li, Ziqiang Xu, Bingyu Xin, Dimitris N. Metaxas, Narges Razizadeh, Shahabedin Nabavi, George Yiasemis, Jonas Teuwen, Zhenxi Zhang, Sha Wang, Chi Zhang, Daniel B. Ennis, Zhihao Xue, Chenxi Hu, Ruru Xu, Ilkay Oksuz, Donghang Lyu, Yanxin Huang, Xinrui Guo, Ruqian Hao, Jaykumar H. Patel, Guanke Cai, Binghua Chen, Yajing Zhang, Sha Hua, Zhensen Chen, Qi Dou, Xiahai Zhuang, Qian Tao, Wenjia Bai, Jing Qin, He Wang, Claudia Prieto, Michael Markl, Alistair Young, Hao Li, Xihong Hu, Lianmin Wu, Xiaobo Qu, Guang Yang, Chengyan Wang

In addition, through a detailed analysis of the results submitted to the challenge, we have also made several findings, including: 1) adaptive prompt-learning embedding is an effective means for achieving strong generalization in reconstruction models; 2) enhanced data consistency based on physics-informed networks is also an effective pathway toward a universal model; 3) traditional evaluation metrics have limitations when assessing ground-truth references with moderate or lower image quality, highlighting the need for subjective evaluation methods.

Benchmarking Image Reconstruction +3

Multi-task SAR Image Processing via GAN-based Unsupervised Manipulation

no code implementations • 2 Aug 2024 • Xuran Hu, Mingzhe Zhu, Ziqiang Xu, Zhenpeng Feng, Ljubisa Stankovic

This paper proposes a novel SAR image processing framework called GAN-based Unsupervised Editing (GUE), aiming to address the following two issues: (1) disentangling semantic directions in the GAN latent space and finding meaningful directions; (2) establishing a comprehensive SAR image processing framework while achieving multiple image processing functions.

CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

1 code implementation • 27 Jun 2024 • Zi Wang, Fanwen Wang, Chen Qin, Jun Lyu, Cheng Ouyang, Shuo Wang, Yan Li, Mengyao Yu, Haoyu Zhang, Kunyuan Guo, Zhang Shi, Qirong Li, Ziqiang Xu, Yajing Zhang, Hao Li, Sha Hua, Binghua Chen, Longyu Sun, Mengting Sun, Qin Li, Ying-Hua Chu, Wenjia Bai, Jing Qin, Xiahai Zhuang, Claudia Prieto, Alistair Young, Michael Markl, He Wang, Lianming Wu, Guang Yang, Xiaobo Qu, Chengyan Wang

To the best of our knowledge, the CMRxRecon2024 dataset is the largest and most protocal-diverse publicly available cardiac k-space dataset.

MRI Reconstruction

The state-of-the-art in Cardiac MRI Reconstruction: Results of the CMRxRecon Challenge in MICCAI 2023

1 code implementation • 1 Apr 2024 • Jun Lyu, Chen Qin, Shuo Wang, Fanwen Wang, Yan Li, Zi Wang, Kunyuan Guo, Cheng Ouyang, Michael Tänzer, Meng Liu, Longyu Sun, Mengting Sun, Qin Li, Zhang Shi, Sha Hua, Hao Li, Zhensen Chen, Zhenlin Zhang, Bingyu Xin, Dimitris N. Metaxas, George Yiasemis, Jonas Teuwen, Liping Zhang, Weitian Chen, Yidong Zhao, Qian Tao, Yanwei Pang, Xiaohan Liu, Artem Razumov, Dmitry V. Dylov, Quan Dou, Kang Yan, Yuyang Xue, Yuning Du, Julia Dietlmeier, Carles Garcia-Cabrera, Ziad Al-Haj Hemidi, Nora Vogt, Ziqiang Xu, Yajing Zhang, Ying-Hua Chu, Weibo Chen, Wenjia Bai, Xiahai Zhuang, Jing Qin, Lianmin Wu, Guang Yang, Xiaobo Qu, He Wang, Chengyan Wang

To address this issue, we organized the Cardiac MRI Reconstruction Challenge (CMRxRecon) in 2023, in collaboration with the 26th International Conference on MICCAI.

MRI Reconstruction

SAR Despeckling via Regional Denoising Diffusion Probabilistic Model

no code implementations • 6 Jan 2024 • Xuran Hu, Ziqiang Xu, Zhihan Chen, Zhengpeng Feng, Mingzhe Zhu, Ljubisa Stankovic

Speckle noise poses a significant challenge in maintaining the quality of synthetic aperture radar (SAR) images, so SAR despeckling techniques have drawn increasing attention.

model Sar Image Despeckling

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

2 code implementations • 19 Sep 2023 • Chengyan Wang, Jun Lyu, Shuo Wang, Chen Qin, Kunyuan Guo, Xinyu Zhang, Xiaotong Yu, Yan Li, Fanwen Wang, Jianhua Jin, Zhang Shi, Ziqiang Xu, Yapeng Tian, Sha Hua, Zhensen Chen, Meng Liu, Mengting Sun, Xutong Kuang, Kang Wang, Haoran Wang, Hao Li, Yinghua Chu, Guang Yang, Wenjia Bai, Xiahai Zhuang, He Wang, Jing Qin, Xiaobo Qu

However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images.

Diagnostic Image Reconstruction

CVNet: Contour Vibration Network for Building Extraction

1 code implementation • CVPR 2022 • Ziqiang Xu, Chunyan Xu, Zhen Cui, Xiangwei Zheng, Jian Yang

The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently.

Model Optimization