no code implementations • 16 Dec 2024 • Yuxuan Sun, Yixuan Si, Chenglu Zhu, Xuan Gong, Kai Zhang, Pingyi Chen, Ye Zhang, Zhongyi Shui, Tao Lin, Lin Yang
Additionally, we develop a specialized pathology CLIP-based visual processor for CPath-Omni, CPath-CLIP, which, for the first time, integrates different vision models and incorporates a large language model as a text encoder to build a more powerful CLIP model, which achieves SOTA performance on nine zero-shot and four few-shot datasets.
no code implementations • 6 Oct 2024 • Xuan Gong, Tianshi Ming, Xinpeng Wang, Zhihua Wei
As we know, both the visual encoder and the Large Language Model (LLM) decoder in LVLMs are Transformer-based, allowing the model to extract visual information and generate text outputs via attention mechanisms.
no code implementations • 5 Mar 2024 • Meng Zheng, Benjamin Planche, Xuan Gong, Fan Yang, Terrence Chen, Ziyan Wu
3D patient body modeling is critical to the success of automated patient positioning for smart medical scanning and operating rooms.
no code implementations • CVPR 2024 • Tianyu Luan, Zhong Li, Lele Chen, Xuan Gong, Lichang Chen, Yi Xu, Junsong Yuan
Then, we calculate the Area Under the Curve (AUC) difference between the two spectrums, so that each frequency band that captures either the overall or detailed shape is equitably considered.
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.
no code implementations • 27 May 2023 • Yuguang Yang, Runtang Guo, Sheng Wu, Yimi Wang, Juan Zhang, Xuan Gong, Baochang Zhang
Although the Class Activation Map (CAM) is widely used to interpret deep model predictions by highlighting object location, it fails to provide insight into the salient features used by the model to make decisions.
1 code implementation • 15 Mar 2023 • Liangchen Song, Zhong Li, Xuan Gong, Lele Chen, Zhang Chen, Yi Xu, Junsong Yuan
We further propose a simple-yet-effective strategy for tuning the frequency to avoid overfitting few-shot inputs: enforcing consistency among the frequency domain of rendered 2D images.
no code implementations • 10 Dec 2022 • Xuan Gong, Liangchen Song, Meng Zheng, Benjamin Planche, Terrence Chen, Junsong Yuan, David Doermann, Ziyan Wu
To date, little attention has been given to multi-view 3D human mesh estimation, despite real-life applicability (e. g., motion capture, sport analysis) and robustness to single-view ambiguities.
no code implementations • 16 Oct 2022 • Xuan Gong, Liangchen Song, Rishi Vedula, Abhishek Sharma, Meng Zheng, Benjamin Planche, Arun Innanje, Terrence Chen, Junsong Yuan, David Doermann, Ziyan Wu
We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation in this work.
no code implementations • 21 Sep 2022 • Liangchen Song, Xuan Gong, Benjamin Planche, Meng Zheng, David Doermann, Junsong Yuan, Terrence Chen, Ziyan Wu
We propose to regularize the estimated motion to be predictable.
no code implementations • 10 Sep 2022 • Xuan Gong, Meng Zheng, Benjamin Planche, Srikrishna Karanam, Terrence Chen, David Doermann, Ziyan Wu
However, on synthetic dense correspondence maps (i. e., IUV) few have been explored since the domain gap between synthetic training data and real testing data is hard to address for 2D dense representation.
no code implementations • 10 Sep 2022 • Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje
Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively train a central model while the training data remains decentralized.
no code implementations • ICCV 2021 • Xuan Gong, Abhishek Sharma, Srikrishna Karanam, Ziyan Wu, Terrence Chen, David Doermann, Arun Innanje
Such decentralized training naturally leads to issues of imbalanced or differing data distributions among the local models and challenges in fusing them into a central model.
no code implementations • 7 Dec 2020 • Xuan Gong, Xin Xia, Wentao Zhu, Baochang Zhang, David Doermann, Lian Zhuo
In recent years, deep learning has dominated progress in the field of medical image analysis.
no code implementations • 24 Nov 2020 • Wenyu Zhao, Teli Ma, Xuan Gong, Baochang Zhang, David Doermann
Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection.
no code implementations • ECCV 2020 • Hanlin Chen, Baochang Zhang, Song Xue, Xuan Gong, Hong Liu, Rongrong Ji, David Doermann
Deep convolutional neural networks (DCNNs) have dominated as the best performers in machine learning, but can be challenged by adversarial attacks.