1 code implementation • 19 Dec 2023 • Chun-Mei Feng, Yang Bai, Tao Luo, Zhen Li, Salman Khan, WangMeng Zuo, Xinxing Xu, Rick Siow Mong Goh, Yong liu
By feeding the retrieved image and question to the VQA model, one can find the images inconsistent with relative caption when the answer by VQA is inconsistent with the answer in the QA pair.
no code implementations • 26 Nov 2023 • Zhihao Yuan, Jinke Ren, Chun-Mei Feng, Hengshuang Zhao, Shuguang Cui, Zhen Li
Building on this, we design a visual program that consists of three types of modules, i. e., view-independent, view-dependent, and functional modules.
1 code implementation • 9 Oct 2023 • Yang Bai, Xinxing Xu, Yong liu, Salman Khan, Fahad Khan, WangMeng Zuo, Rick Siow Mong Goh, Chun-Mei Feng
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.
Ranked #1 on Image Retrieval on CIRR
no code implementations • 21 Aug 2023 • Xinghong Liu, Yi Zhou, Tao Zhou, Chun-Mei Feng, Ling Shao
SF-UniDA methods eliminate the need for direct access to source samples when performing adaptation to the target domain.
no code implementations • 20 Aug 2023 • Yunlu Yan, Chun-Mei Feng, Mang Ye, WangMeng Zuo, Ping Li, Rick Siow Mong Goh, Lei Zhu, C. L. Philip Chen
Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype.
no code implementations • 20 Aug 2023 • Yunlu Yan, Chun-Mei Feng, Yuexiang Li, Rick Siow Mong Goh, Lei Zhu
In this paper, we propose a novel communication-efficient federated learning framework, namely Fed-PMG, to address the missing modality challenge in federated multi-modal MRI reconstruction.
1 code implementation • ICCV 2023 • Chun-Mei Feng, Kai Yu, Yong liu, Salman Khan, WangMeng Zuo
In this paper, we focus on a particular setting of learning adaptive prompts on the fly for each test sample from an unseen new domain, which is known as test-time prompt tuning (TPT).
1 code implementation • ICCV 2023 • Chun-Mei Feng, Kai Yu, Nian Liu, Xinxing Xu, Salman Khan, WangMeng Zuo
However, the performance of the global model is often hampered by non-i. i. d.
1 code implementation • CVPR 2023 • Chun-Mei Feng, Bangjun Li, Xinxing Xu, Yong liu, Huazhu Fu, WangMeng Zuo
Federated Magnetic Resonance Imaging (MRI) reconstruction enables multiple hospitals to collaborate distributedly without aggregating local data, thereby protecting patient privacy.
no code implementations • 30 Jan 2023 • Meng Wang, Kai Yu, Chun-Mei Feng, Yiming Qian, Ke Zou, Lianyu Wang, Rick Siow Mong Goh, Yong liu, Huazhu Fu
To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling, which enhances the performance and reliability of FL in non-IID domain features.
1 code implementation • 10 Dec 2022 • Ruohao Wang, Xiaohui Liu, Zhilu Zhang, Xiaohe Wu, Chun-Mei Feng, Lei Zhang, WangMeng Zuo
On the other hand, alignment algorithms in existing VSR methods perform poorly for real-world videos, leading to unsatisfactory results.
no code implementations • 1 Dec 2022 • Meng Wang, Kai Yu, Chun-Mei Feng, Ke Zou, Yanyu Xu, Qingquan Meng, Rick Siow Mong Goh, Yong liu, Huazhu Fu
Specifically, aiming at improving the model's ability to learn the complex pathological features of retinal edema lesions in OCT images, we develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module of our newly designed.
no code implementations • 28 Sep 2022 • Haoran Li, Chun-Mei Feng, Tao Zhou, Yong Xu, Xiaojun Chang
In this paper, we propose a prompt-driven efficient OSSL framework, called OpenPrompt, which can propagate class information from labeled to unlabeled data with only a small number of trainable parameters.
1 code implementation • 9 Dec 2021 • Chun-Mei Feng, Yunlu Yan, Shanshan Wang, Yong Xu, Ling Shao, Huazhu Fu
The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution.
1 code implementation • journal 2021 • Tianfei Zhou, Liulei Li, Xueyi Li, Chun-Mei Feng, Jianwu Li, Ling Shao
The framework explicitly encodes semantic dependencies in a group of images to discover rich semantic context for estimating more reliable pseudo ground-truths, which are subsequently employed to train more effective segmentation models.
2 code implementations • 15 Oct 2021 • Chun-Mei Feng, Huazhu Fu, Tianfei Zhou, Yong Xu, Ling Shao, David Zhang
Magnetic resonance (MR) imaging produces detailed images of organs and tissues with better contrast, but it suffers from a long acquisition time, which makes the image quality vulnerable to say motion artifacts.
1 code implementation • 3 Sep 2021 • Chun-Mei Feng, Yunlu Yan, Kai Yu, Yong Xu, Ling Shao, Huazhu Fu
Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image.
1 code implementation • 27 Jun 2021 • Chun-Mei Feng, Yunlu Yan, Geng Chen, Yong Xu, Ling Shao, Huazhu Fu
To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging.
1 code implementation • 12 Jun 2021 • Chun-Mei Feng, Yunlu Yan, Huazhu Fu, Li Chen, Yong Xu
Then, a task transformer module is designed to embed and synthesize the relevance between the two tasks.
Ranked #9 on Image Super-Resolution on IXI
1 code implementation • 19 May 2021 • Chun-Mei Feng, Huazhu Fu, Shuhao Yuan, Yong Xu
In this work, we propose a multi-stage integration network (i. e., MINet) for multi-contrast MRI SR, which explicitly models the dependencies between multi-contrast images at different stages to guide image SR.
no code implementations • 12 May 2021 • Chun-Mei Feng, Zhanyuan Yang, Huazhu Fu, Yong Xu, Jian Yang, Ling Shao
In this paper, we propose the Dual-Octave Network (DONet), which is capable of learning multi-scale spatial-frequency features from both the real and imaginary components of MR data, for fast parallel MR image reconstruction.
1 code implementation • 12 Apr 2021 • Chun-Mei Feng, Zhanyuan Yang, Geng Chen, Yong Xu, Ling Shao
We evaluate the performance of the proposed model on the acceleration of multi-coil MR image reconstruction.
no code implementations • 12 Jul 2019 • Chun-Mei Feng, Kai Wang, Shijian Lu, Yong Xu, Heng Kong, Ling Shao
The deep sub-network learns from the residuals of the high-frequency image information, where multiple residual blocks are cascaded to magnify the MRI images at the last network layer.
no code implementations • 10 Jun 2019 • Chun-Mei Feng, Yong Xu, Zuoyong Li, Jian Yang
It performs Sparse Representation Fusion based on the Diverse Subset of training samples (SRFDS), which reduces the impact of randomness of the sample set and enhances the robustness of classification results.
no code implementations • 28 May 2019 • Chun-Mei Feng, Yong Xu, Jin-Xing Liu, Ying-Lian Gao, Chun-Hou Zheng
To overcome this problem, this study developed a new PCA method, which is named the Supervised Discriminative Sparse PCA (SDSPCA).