1 code implementation • 30 Apr 2024 • Jie Hu, Yawen Huang, Yilin Lu, Guoyang Xie, Guannan Jiang, Yefeng Zheng, Zhichao Lu
The AnomalyXFusion framework comprises two distinct yet synergistic modules: the Multi-modal In-Fusion (MIF) module and the Dynamic Dif-Fusion (DDF) module.
1 code implementation • 24 Apr 2024 • Jinheng Xie, Jiajun Feng, Zhaoxu Tian, Kevin Qinghong Lin, Yawen Huang, Xi Xia, Nanxu Gong, Xu Zuo, Jiaqi Yang, Yefeng Zheng, Mike Zheng Shou
Instead of operating on pixel space, it is efficient to employ visual locations like bounding boxes and keypoints to represent key information in videos, which can be simply discretized and then tokenized for consumption by GPT.
no code implementations • 7 Apr 2024 • Peng Tu, Xun Zhou, Mingming Wang, Xiaojun Yang, Bo Peng, Ping Chen, Xiu Su, Yawen Huang, Yefeng Zheng, Chang Xu
Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoints with remarkable fidelity.
1 code implementation • 2 Feb 2024 • Xingyu Miao, Yang Bai, Haoran Duan, Fan Wan, Yawen Huang, Yang Long, Yefeng Zheng
Most of the existing works on arbitrary 3D NeRF style transfer required retraining on each single style condition.
no code implementations • 10 Jan 2024 • Xingyu Miao, Yang Bai, Haoran Duan, Yawen Huang, Fan Wan, Yang Long, Yefeng Zheng
The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes.
1 code implementation • 22 Dec 2023 • Xuan Gong, Shanglin Li, Yuxiang Bao, Barry Yao, Yawen Huang, Ziyan Wu, Baochang Zhang, Yefeng Zheng, David Doermann
Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without sharing individually held private data.
1 code implementation • 23 Sep 2023 • Hantao Zhou, Rui Yang, Yachao Zhang, Haoran Duan, Yawen Huang, Runze Hu, Xiu Li, Yefeng Zheng
More precisely, our approach (1) introduces deformation perception, enabling the model to adaptively sample object features; (2) proposes a Dual-axial Aggregation Transformer (DAT) to adeptly model long-range dependencies, thereby achieving global perception; and (3) devises a Cross-task Interaction Transformer (CIT) that facilitates interaction between the classification and localization branches, thus aligning the two tasks.
1 code implementation • 22 Sep 2023 • Dong Wei, Yawen Huang, Donghuan Lu, Yuexiang Li, Yefeng Zheng
Then, a multi-view planning strategy is proposed to aggregate information from the predicted heatmaps for all the source views of a target plane, for a globally optimal prescription, mimicking the similar strategy practiced by skilled human prescribers.
1 code implementation • 14 Aug 2023 • Xingyu Miao, Yang Bai, Haoran Duan, Yawen Huang, Fan Wan, Xinxing Xu, Yang Long, Yefeng Zheng
Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other.
2 code implementations • ICCV 2023 • Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, Mike Zheng Shou
As such paired data is time-consuming and labor-intensive to acquire and restricted to a closed set, this potentially becomes the bottleneck for applications in an open world.
Ranked #5 on Conditional Text-to-Image Synthesis on COCO-MIG
no code implementations • 10 Jul 2023 • Guoyang Xie, Jinbao Wang, Yawen Huang, Jiayi Lyu, Feng Zheng, Yefeng Zheng, Yaochu Jin
To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty.
1 code implementation • 13 Jun 2023 • Wentian Zhang, Haozhe Liu, Bing Li, Jinheng Xie, Yawen Huang, Yuexiang Li, Yefeng Zheng, Bernard Ghanem
By treating the generated data in training as a stream, we propose to detect whether the discriminator slows down the learning of new knowledge in generated data.
no code implementations • 5 Jun 2023 • Yunlu Yan, Hong Wang, Yawen Huang, Nanjun He, Lei Zhu, Yuexiang Li, Yong Xu, Yefeng Zheng
To this end, we formulate this practical-yet-challenging cross-modal vertical federated learning task, in which shape data from multiple hospitals have different modalities with a small amount of multi-modality data collected from the same individuals.
1 code implementation • 2 Mar 2023 • Haozhe Liu, Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
1 code implementation • 28 Feb 2023 • Minghao Zhou, Hong Wang, Qian Zhao, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng
Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
1 code implementation • ICCV 2023 • Peng Tu, Xu Xie, Guo Ai, Yuexiang Li, Yawen Huang, Yefeng Zheng
Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors.
Ranked #30 on Object Detection on PASCAL VOC 2007
no code implementations • 3 Jan 2023 • Yuexiang Li, Yawen Huang, Nanjun He, Kai Ma, Yefeng Zheng
The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.
1 code implementation • CVPR 2023 • Haozhe Liu, Wentian Zhang, Bing Li, Haoqian Wu, Nanjun He, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng
The evaluation results demonstrate that our AdaptiveMix can facilitate the training of GANs and effectively improve the image quality of generated samples.
1 code implementation • CVPR 2023 • Minghao Zhou, Hong Wang, Qian Zhao, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng
Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
no code implementations • CVPR 2023 • Huimin Huang, Shiao Xie, Lanfen Lin, Ruofeng Tong, Yen-Wei Chen, Yuexiang Li, Hong Wang, Yawen Huang, Yefeng Zheng
Semi-supervised learning improves data efficiency of deep models by leveraging unlabeled samples to alleviate the reliance on a large set of labeled samples.
1 code implementation • 26 Dec 2022 • Hong Wang, Qi Xie, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment.
no code implementations • 2 Nov 2022 • Peng Zhang, Yawen Huang, Bingzhang Hu, Shizheng Wang, Haoran Duan, Noura Al Moubayed, Yefeng Zheng, Yang Long
Reinforcement Learning (RL)-based control system has received considerable attention in recent decades.
1 code implementation • 26 Oct 2022 • Haozhe Liu, Wentian Zhang, Jinheng Xie, Haoqian Wu, Bing Li, Ziqi Zhang, Yuexiang Li, Yawen Huang, Bernard Ghanem, Yefeng Zheng
Since the observation is that noise-prone regions such as textural and clutter backgrounds are adverse to the generalization ability of CNN models during training, we enhance features from discriminative regions and suppress noise-prone ones when combining an image pair.
1 code implementation • 5 Sep 2022 • Haoqin Ji, Haozhe Liu, Yuexiang Li, Jinheng Xie, Nanjun He, Yawen Huang, Dong Wei, Xinrong Chen, Linlin Shen, Yefeng Zheng
Such a point annotation setting can provide weakly instance-level information for abnormality localization with a marginal annotation cost.
1 code implementation • 25 Aug 2022 • Haozhe Liu, Bing Li, Haoqian Wu, Hanbang Liang, Yawen Huang, Yuexiang Li, Bernard Ghanem, Yefeng Zheng
In this paper, we propose a novel training pipeline to address the mode collapse issue of GANs.
1 code implementation • 6 Jun 2022 • Yao Zhang, Nanjun He, Jiawei Yang, Yuexiang Li, Dong Wei, Yawen Huang, Yang Zhang, Zhiqiang He, Yefeng Zheng
Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incomplete multimodal learning with three main components: the hybrid modality-specific encoders that bridge a convolutional encoder and an intra-modal Transformer for both local and global context modeling within each modality; an inter-modal Transformer to build and align the long-range correlations across modalities for modality-invariant features with global semantics corresponding to tumor region; a decoder that performs a progressive up-sampling and fusion with the modality-invariant features to generate robust segmentation.
Ranked #65 on Semantic Segmentation on NYU Depth v2
no code implementations • 14 Feb 2022 • Guoyang Xie, Yawen Huang, Jinbao Wang, Jiayi Lyu, Feng Zheng, Yefeng Zheng, Yaochu Jin
This is followed by a stepwise in-depth analysis to evaluate how cross-modality neuroimage synthesis improves the performance of its downstream tasks.
1 code implementation • 29 Jan 2022 • Jinbao Wang, Guoyang Xie, Yawen Huang, Yefeng Zheng, Yaochu Jin, Feng Zheng
The proposed method demonstrates the advanced performance in both the quality of our synthesized results under a severely misaligned and unpaired data setting, and better stability than other GAN-based algorithms.
1 code implementation • 22 Jan 2022 • Jinbao Wang, Guoyang Xie, Yawen Huang, Jiayi Lyu, Yefeng Zheng, Feng Zheng, Yaochu Jin
There is a clear need to launch a federated learning and facilitate the integration of the dispersed data from different institutions.
no code implementations • 28 Dec 2021 • Peng Tu, Yawen Huang, Feng Zheng, Zhenyu He, Liujun Cao, Ling Shao
In this paper, we propose a novel method for semi-supervised semantic segmentation named GuidedMix-Net, by leveraging labeled information to guide the learning of unlabeled instances.
1 code implementation • 29 Jun 2021 • Peng Tu, Yawen Huang, Rongrong Ji, Feng Zheng, Ling Shao
To take advantage of the labeled examples and guide unlabeled data learning, we further propose a mask generation module to generate high-quality pseudo masks for the unlabeled data.
no code implementations • CVPR 2021 • Yawen Huang, Feng Zheng, Danyang Wang, Weilin Huang, Matthew R. Scott, Ling Shao
Recent advances in neuroscience have highlighted the effectiveness of multi-modal medical data for investigating certain pathologies and understanding human cognition.
no code implementations • 22 Mar 2021 • Yawen Huang, Feng Zheng, Danyang Wang, Weilin Huang, Matthew R. Scott, Ling Shao
Recent advances in neuroscience have highlighted the effectiveness of multi-modal medical data for investigating certain pathologies and understanding human cognition.
no code implementations • 15 Jun 2017 • Yawen Huang, Ling Shao, Alejandro F. Frangi
Cross-modal image synthesis is a topical problem in medical image computing.
no code implementations • CVPR 2017 • Yawen Huang, Ling Shao, Alejandro F. Frangi
We propose the weakly-supervised joint convolutional sparse coding to simultaneously solve the problems of super-resolution (SR) and cross-modality image synthesis.