no code implementations • 14 Jun 2023 • Zhiyuan Hu, Jiancheng Lyu, Dashan Gao, Nuno Vasconcelos
We show that a foundation model equipped with POP learning is able to outperform classic CL methods by a significant margin.
no code implementations • 9 May 2023 • Sheng Wan, Dashan Gao, Hanlin Gu, Daning Hu
However, in reality, the number of overlapped users is often very small, thus largely limiting the performance of such approaches.
no code implementations • 8 Apr 2023 • Dashan Gao, Yunce Zhao, Yinghua Yao, Zeqi Zhang, Bifei Mao, Xin Yao
In this paper, we study the robustness of deep learning models against joint perturbations by proposing a novel attack mechanism named Semantic-Preserving Adversarial (SPA) attack, which can then be used to enhance adversarial training.
no code implementations • 30 Mar 2023 • Renhong Zhang, Tianheng Cheng, Shusheng Yang, Haoyi Jiang, Shuai Zhang, Jiancheng Lyu, Xin Li, Xiaowen Ying, Dashan Gao, Wenyu Liu, Xinggang Wang
To address those issues, we present MobileInst, a lightweight and mobile-friendly framework for video instance segmentation on mobile devices.
no code implementations • CVPR 2023 • Zhiyuan Hu, Yunsheng Li, Jiancheng Lyu, Dashan Gao, Nuno Vasconcelos
This is accomplished by the introduction of dense connections between the intermediate layers of the task expert networks, that enable the transfer of knowledge from old to new tasks via feature sharing and reusing.
no code implementations • 10 Oct 2022 • Dashan Gao, Xin Yao, Qiang Yang
Therefore, heterogeneous FL is proposed to address the problem of heterogeneity in FL.
no code implementations • 3 Jul 2020 • Dashan Gao, Ben Tan, Ce Ju, Vincent W. Zheng, Qiang Yang
Matrix Factorization has been very successful in practical recommendation applications and e-commerce.
no code implementations • 15 Jun 2020 • Ce Ju, Ruihui Zhao, Jichao Sun, Xiguang Wei, Bo Zhao, Yang Liu, Hongshan Li, Tianjian Chen, Xinwei Zhang, Dashan Gao, Ben Tan, Han Yu, Chuning He, Yuan Jin
It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting.
1 code implementation • 26 Apr 2020 • Ce Ju, Dashan Gao, Ravikiran Mane, Ben Tan, Yang Liu, Cuntai Guan
The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets.
no code implementations • 4 Apr 2020 • Xudong Wang, Shizhong Han, Yunqiang Chen, Dashan Gao, Nuno Vasconcelos
A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed.
1 code implementation • 11 Feb 2020 • Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass
Medical images are increasingly used as input to deep neural networks to produce quantitative values that aid researchers and clinicians.
2 code implementations • 11 Feb 2020 • Jacob C. Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry L. Prince, Aaron Carass
Medical images are often used to detect and characterize pathology and disease; however, automatically identifying and segmenting pathology in medical images is challenging because the appearance of pathology across diseases varies widely.
no code implementations • 10 Feb 2020 • Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods.
no code implementations • 10 Feb 2020 • Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e. g., exemplars for which the baseline algorithm failed) or inliers (e. g., exemplars for which the baseline algorithm worked).
no code implementations • 1 Dec 2019 • Yuankai Huo, Yucheng Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Shunxing Bao, Smita De, James G. Terry, Jeffrey J. Carr, Richard G. Abramson, Bennett A. Landman
We evaluate the effectiveness of both with and without using soft tissue window normalization on multisite CT cohorts.
no code implementations • 14 Nov 2019 • Yucheng Tang, Ho Hin Lee, Yuchen Xu, Olivia Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Camilo Bermudez, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy.
no code implementations • 12 Nov 2019 • Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael R. Savona, Richard G. Abramson, Yuankai Huo, Bennett A. Landman
The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method.
1 code implementation • 11 Sep 2019 • Dashan Gao, Ce Ju, Xiguang Wei, Yang Liu, Tianjian Chen, Qiang Yang
To verify the effectiveness of our approach, we conduct experiments on a real-world EEG dataset, consisting of heterogeneous data collected from diverse devices.
1 code implementation • CVPR 2019 • Xudong Wang, Zhaowei Cai, Dashan Gao, Nuno Vasconcelos
Experiments, on a newly established universal object detection benchmark of 11 diverse datasets, show that the proposed detector outperforms a bank of individual detectors, a multi-domain detector, and a baseline universal detector, with a 1. 3x parameter increase over a single-domain baseline detector.
no code implementations • CVPR 2015 • Mandar Dixit, Si Chen, Dashan Gao, Nikhil Rasiwasia, Nuno Vasconcelos
A semantic FV is then computed as a Gaussian Mixture FV in the space of these natural parameters.
no code implementations • NeurIPS 2007 • Dashan Gao, Vijay Mahadevan, Nuno Vasconcelos
The classical hypothesis, that bottom-up saliency is a center-surround process, is combined with a more recent hypothesis that all saliency decisions are optimal in a decision-theoretic sense.