no code implementations • 16 Nov 2022 • Linus Ericsson, Nanqing Dong, Yongxin Yang, Ales Leonardis, Steven McDonagh
In this work, we propose a simple pretext task that provides an effective pre-training for the RPN, towards efficiently improving downstream object detection performance.
no code implementations • 5 Nov 2022 • Nanqing Dong, Jiahao Sun, Zhipeng Wang, Shuoying Zhang, Shuhao Zheng
Federated learning (FL) is a promising way to allow multiple data owners (clients) to collaboratively train machine learning models without compromising data privacy.
no code implementations • 30 Jun 2022 • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu
In the second stage, the decentralized partially labeled data are exploited to learn an energy-based multi-label classifier for the common classes.
no code implementations • 19 Apr 2022 • Nanqing Dong, Jiayi Wang, Irina Voiculescu
Due to the high human cost of annotation, it is non-trivial to curate a large-scale medical dataset that is fully labeled for all classes of interest.
no code implementations • 6 Oct 2021 • Zhaoming Qin, Nanqing Dong, Eric P. Xing, Junwei Cao
As a scalable data-driven approach, multi-agent reinforcement learning (MARL) has made remarkable advances in solving the cooperative residential load scheduling problems.
no code implementations • 29 Sep 2021 • Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh
In the era of deep learning, supervised residual learning (ResL) has led to many breakthroughs in low-level vision such as image restoration and enhancement tasks.
no code implementations • 29 Sep 2021 • Nanqing Dong, Jianwen Xie, Ping Li
We present a simple yet robust noise synthesis framework based on unsupervised contrastive learning.
no code implementations • 15 Sep 2021 • Nanqing Dong, Irina Voiculescu
A label-efficient paradigm in computer vision is based on self-supervised contrastive pre-training on unlabeled data followed by fine-tuning with a small number of labels.
no code implementations • 18 Jun 2021 • Nanqing Dong, Matteo Maggioni, Yongxin Yang, Eduardo Pérez-Pellitero, Ales Leonardis, Steven McDonagh
We propose a new label-efficient learning paradigm based on residuals, residual contrastive learning (RCL), and derive an unsupervised visual representation learning framework, suitable for low-level vision tasks with noisy inputs.
1 code implementation • 20 May 2021 • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing
In this work, we provide some theoretical insight into the properties of QNNs by presenting and analyzing a new form of invariance embedded in QNNs for both quantum binary classification and quantum representation learning, which we term negational symmetry.
no code implementations • 28 Nov 2020 • Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, Eric P. Xing
To bridge the methodological gaps in partially supervised learning (PSL) under data scarcity, we propose Vicinal Labels Under Uncertainty (VLUU), a simple yet efficient framework utilizing the human structure similarity for partially supervised medical image segmentation.
no code implementations • 28 May 2019 • Zeya Wang, Baoyu Jing, Yang Ni, Nanqing Dong, Pengtao Xie, Eric P. Xing
In this paper, we propose a novel relationship-aware adversarial domain adaptation (RADA) algorithm, which first utilizes a single multi-class domain discriminator to enforce the learning of inter-class dependency structure during domain-adversarial training and then aligns this structure with the inter-class dependencies that are characterized from training the label predictor on source domain.
no code implementations • ICLR 2019 • Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing
Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates.
no code implementations • 29 Jul 2018 • Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing
Motivated by the zoom-in operation of a pathologist using a digital microscope, RAZN learns a policy network to decide whether zooming is required in a given region of interest.
no code implementations • 10 Jul 2018 • Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Zeya Wang, Wei Dai, Eric P. Xing
Specifically, we propose a model that enforces our intuition that prediction masks should be domain independent.
no code implementations • 20 Apr 2018 • Michael Kampffmeyer, Nanqing Dong, Xiaodan Liang, Yu-jia Zhang, Eric P. Xing
We argue that semantic salient segmentation can instead be effectively resolved by reformulating it as a simple yet intuitive pixel-pair based connectivity prediction task.
no code implementations • 26 Mar 2017 • Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, Eric P. Xing
Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes.