Search Results for author: Xiaoxiao Li

Found 84 papers, 36 papers with code

Client-Level Differential Privacy via Adaptive Intermediary in Federated Medical Imaging

1 code implementation24 Jul 2023 Meirui Jiang, Yuan Zhong, Anjie Le, Xiaoxiao Li, Qi Dou

In this paper, we propose to optimize the trade-off under the context of client-level DP, which focuses on privacy during communications.

Federated Learning

FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

no code implementations20 Jul 2023 Minghui Chen, Meirui Jiang, Qi Dou, Zehua Wang, Xiaoxiao Li

In this paper, we propose a novel federated model soup method (i. e., selective interpolation of model parameters) to optimize the trade-off between local and global performance.

Federated Learning Image Classification +1

Community-Aware Transformer for Autism Prediction in fMRI Connectome

1 code implementation24 Jun 2023 Anushree Bannadabhavi, Soojin Lee, Wenlong Deng, Xiaoxiao Li

Investigating functional magnetic resonance imaging (fMRI)-based brain functional connectome can aid in the understanding and diagnosis of ASD, leading to more effective treatments.

Forgettable Federated Linear Learning with Certified Data Removal

no code implementations3 Jun 2023 Ruinan Jin, Minghui Chen, Qiong Zhang, Xiaoxiao Li

To this end, we propose the Forgettable Federated Linear Learning (2F2L) framework featured with novel training and data removal strategies.

Federated Learning

Scalable Data Point Valuation in Decentralized Learning

1 code implementation1 May 2023 Konstantin D. Pandl, Chun-Yin Huang, Ivan Beschastnikh, Xiaoxiao Li, Scott Thiebes, Ali Sunyaev

The valuation of data points through DDVal allows to also draw hierarchical conclusions on the contribution of institutions, and we empirically show that the accuracy of DDVal in estimating institutional contributions is higher than existing Shapley value approximation methods for federated learning.

Data Valuation Federated Learning

Matching-based Data Valuation for Generative Model

no code implementations21 Apr 2023 Jiaxi Yang, Wenglong Deng, Benlin Liu, Yangsibo Huang, Xiaoxiao Li

To the best of their knowledge, GMValuator is the first work that offers a training-free, post-hoc data valuation strategy for deep generative models.

Data Valuation

Biological Factor Regulatory Neural Network

1 code implementation11 Apr 2023 Xinnan Dai, Caihua Shan, Jie Zheng, Xiaoxiao Li, Dongsheng Li

BFReg-NN starts from gene expression data and is capable of merging most existing biological knowledge into the model, including the regulatory relations among genes or proteins (e. g., gene regulatory networks (GRN), protein-protein interaction networks (PPI)) and the hierarchical relations among genes, proteins and pathways (e. g., several genes/proteins are contained in a pathway).

The XAI Alignment Problem: Rethinking How Should We Evaluate Human-Centered AI Explainability Techniques

no code implementations30 Mar 2023 Weina Jin, Xiaoxiao Li, Ghassan Hamarneh

Optimizing XAI for plausibility regardless of the model decision correctness also jeopardizes model trustworthiness, because doing so breaks an important assumption in human-human explanation that plausible explanations typically imply correct decisions, and vice versa; and violating this assumption eventually leads to either undertrust or overtrust of AI models.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1

Federated Virtual Learning on Heterogeneous Data with Local-global Distillation

1 code implementation4 Mar 2023 Chun-Yin Huang, Ruinan Jin, Can Zhao, Daguang Xu, Xiaoxiao Li

To address this, we propose a new method, called Federated Virtual Learning on Heterogeneous Data with Local-Global Distillation (FedLGD), which trains FL using a smaller synthetic dataset (referred as virtual data) created through a combination of local and global dataset distillation.

Federated Learning

Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation

no code implementations3 Mar 2023 Yilin Yang, Kamil Adamczewski, Danica J. Sutherland, Xiaoxiao Li, Mijung Park

Maximum mean discrepancy (MMD) is a particularly useful distance metric for differentially private data generation: when used with finite-dimensional features it allows us to summarize and privatize the data distribution once, which we can repeatedly use during generator training without further privacy loss.

Privacy Preserving

SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation

1 code implementation16 Dec 2022 Jee Seok Yoon, Chenghao Zhang, Heung-Il Suk, Jia Guo, Xiaoxiao Li

To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images.

Image Generation Medical Image Generation

Robust Split Federated Learning for U-shaped Medical Image Networks

1 code implementation13 Dec 2022 Ziyuan Yang, Yingyu Chen, Huijie Huangfu, Maosong Ran, Hui Wang, Xiaoxiao Li, Yi Zhang

To achieve this goal, in this paper, we propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks, which is a novel hybrid learning paradigm of FL and SL.

Federated Learning

A Convergence Theory for Federated Average: Beyond Smoothness

no code implementations3 Nov 2022 Xiaoxiao Li, Zhao Song, Runzhou Tao, Guangyi Zhang

As a leading algorithm in this setting, Federated Average FedAvg, which runs Stochastic Gradient Descent (SGD) in parallel on local devices and averages the sequences only once in a while, have been widely used due to their simplicity and low communication cost.

Edge-computing Federated Learning

Improving Fairness in Image Classification via Sketching

1 code implementation31 Oct 2022 Ruichen Yao, Ziteng Cui, Xiaoxiao Li, Lin Gu

Fairness is a fundamental requirement for trustworthy and human-centered Artificial Intelligence (AI) system.

Classification Fairness +1

Backdoor Attack and Defense in Federated Generative Adversarial Network-based Medical Image Synthesis

no code implementations19 Oct 2022 Ruinan Jin, Xiaoxiao Li

However, given that the FL server cannot access the raw data, it is vulnerable to backdoor attacks, an adversarial by poisoning training data.

Backdoor Attack Data Augmentation +3

FedMT: Federated Learning with Mixed-type Labels

no code implementations5 Oct 2022 Qiong Zhang, Aline Talhouk, Gang Niu, Xiaoxiao Li

In this paper, we consider an important yet under-explored setting of FL, namely FL with mixed-type labels where different labeling criteria can be employed by various centers, leading to inter-center label space differences and challenging existing FL methods designed for the classical setting.

Federated Learning Vocal Bursts Type Prediction

Efficient Medical Image Assessment via Self-supervised Learning

no code implementations28 Sep 2022 Chun-Yin Huang, Qi Lei, Xiaoxiao Li

Existing data assessment methods commonly require knowing the labels in advance, which are not feasible to achieve our goal of 'knowing which data to label.'

Self-Supervised Learning

Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels

no code implementations27 Sep 2022 Chenyu You, Weicheng Dai, Fenglin Liu, Yifei Min, Haoran Su, Xiaoran Zhang, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan

Blindly leveraging all pixels in training hence can lead to the data imbalance issues, and cause deteriorated performance; (2) consistency: it remains unclear whether a segmentation model has learned meaningful and yet consistent anatomical features due to the intra-class variations between different anatomical features; and (3) diversity: the intra-slice correlations within the entire dataset have received significantly less attention.

Anatomy Contrastive Learning +3

Transcending XAI Algorithm Boundaries through End-User-Inspired Design

no code implementations18 Aug 2022 Weina Jin, Jianyu Fan, Diane Gromala, Philippe Pasquier, Xiaoxiao Li, Ghassan Hamarneh

The boundaries of existing explainable artificial intelligence (XAI) algorithms are confined to problems grounded in technical users' demand for explainability.

Autonomous Driving Counterfactual Explanation +2

Federated Adversarial Learning: A Framework with Convergence Analysis

no code implementations7 Aug 2022 Xiaoxiao Li, Zhao Song, Jiaming Yang

Unlike the convergence analysis in classical centralized training that relies on the gradient direction, it is significantly harder to analyze the convergence in FAL for three reasons: 1) the complexity of min-max optimization, 2) model not updating in the gradient direction due to the multi-local updates on the client-side before aggregation and 3) inter-client heterogeneity.

Federated Learning

Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection

1 code implementation5 Aug 2022 Ziteng Cui, Yingying Zhu, Lin Gu, Guo-Jun Qi, Xiaoxiao Li, Renrui Zhang, Zenghui Zhang, Tatsuya Harada

Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images.

Image Restoration object-detection +3

Backdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis

1 code implementation2 Jul 2022 Ruinan Jin, Xiaoxiao Li

In this study, we propose a way of attacking federated GAN (FedGAN) by treating the discriminator with a commonly used data poisoning strategy in backdoor attack classification models.

Backdoor Attack Data Poisoning +4

Interpretable Graph Neural Networks for Connectome-Based Brain Disorder Analysis

1 code implementation30 Jun 2022 Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang

Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience.

Disease Prediction

Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

1 code implementation27 Jun 2022 Meirui Jiang, Hongzheng Yang, Xiaoxiao Li, Quande Liu, Pheng-Ann Heng, Qi Dou

Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use.

Federated Learning

Class Impression for Data-free Incremental Learning

no code implementations26 Jun 2022 Sana Ayromlou, Purang Abolmaesumi, Teresa Tsang, Xiaoxiao Li

Here, we propose a novel data-free class incremental learning framework that first synthesizes data from the model trained on previous classes to generate a \ours.

class-incremental learning Class Incremental Learning +1

Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging

1 code implementation8 Jun 2022 Ziyuan Yang, Wenjun Xia, Zexin Lu, Yingyu Chen, Xiaoxiao Li, Yi Zhang

The basic assumption of HyperFed is that the optimization problem for each institution can be divided into two parts: the local data adaption problem and the global CT imaging problem, which are implemented by an institution-specific hypernetwork and a global-sharing imaging network, respectively.

Computed Tomography (CT) Personalized Federated Learning

Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients

1 code implementation7 Apr 2022 Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama

We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model.

Federated Learning

GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis

1 code implementation17 Mar 2022 Liang Peng, Nan Wang, Jie Xu, Xiaofeng Zhu, Xiaoxiao Li

To improve fMRI representation learning and classification under a label-efficient setting, we propose a novel and theory-driven self-supervised learning (SSL) framework on GCNs, namely Graph CCA for Temporal self-supervised learning on fMRI analysis GATE.

Classification Representation Learning +1

Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis

1 code implementation16 Feb 2022 Weina Jin, Xiaoxiao Li, Mostafa Fatehi, Ghassan Hamarneh

Following the guidelines, we conducted a systematic evaluation on a novel problem of multi-modal medical image explanation with two clinical tasks, and proposed new evaluation metrics accordingly.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images

no code implementations7 Jan 2022 Ziteng Cui, Yingying Zhu, Lin Gu, Guo-Jun Qi, Xiaoxiao Li, Peng Gao, Zenghui Zhang, Tatsuya Harada

Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in degraded images.

Image Restoration object-detection +3

Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data

no code implementations2 Dec 2021 Yifei HUANG, Xiaoxiao Li, Lijin Yang, Lin Gu, Yingying Zhu, Hirofumi Seo, Qiuming Meng, Tatsuya Harada, Yoichi Sato

Then we design a novel Auxiliary Attention Block (AAB) to allow information from SAN to be utilized by the backbone encoder to focus on selective areas.

Tumor Segmentation

UniFed: A Unified Framework for Federated Learning on Non-IID Image Features

no code implementations19 Oct 2021 Meirui Jiang, Xiaoxiao Li, Xiaofei Zhang, Michael Kamp, Qi Dou

In this work, we propose a unified framework to tackle the non-iid issues for internal and external clients together.

Domain Generalization Federated Learning +1

SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition

no code implementations8 Oct 2021 Li Fu, Xiaoxiao Li, Runyu Wang, Lu Fan, Zhengchen Zhang, Meng Chen, Youzheng Wu, Xiaodong He

End-to-end Automatic Speech Recognition (ASR) models are usually trained to optimize the loss of the whole token sequence, while neglecting explicit phonemic-granularity supervision.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Provable Federated Adversarial Learning via Min-max Optimization

no code implementations29 Sep 2021 Xiaoxiao Li, Zhao Song, Jiaming Yang

Unlike the convergence analysis in centralized training that relies on the gradient direction, it is significantly harder to analyze the convergence in FAL for two reasons: 1) the complexity of min-max optimization, and 2) model not updating in the gradient direction due to the multi-local updates on the client-side before aggregation.

Federated Learning

Unsupervised Federated Learning is Possible

no code implementations ICLR 2022 Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou, Masashi Sugiyama

We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model.

Federated Learning

On the Fairness of Swarm Learning in Skin Lesion Classification

no code implementations24 Sep 2021 Di Fan, Yifan Wu, Xiaoxiao Li

Distributed and collaborative learning is an approach to involve training models in massive, heterogeneous, and distributed data sources, also known as nodes.

Classification Edge-computing +3

EMA: Auditing Data Removal from Trained Models

1 code implementation8 Sep 2021 Yangsibo Huang, Xiaoxiao Li, Kai Li

In this paper, we propose a new method called Ensembled Membership Auditing (EMA) for auditing data removal to overcome these limitations.

BrainNNExplainer: An Interpretable Graph Neural Network Framework for Brain Network based Disease Analysis

1 code implementation11 Jul 2021 Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang

Interpretable brain network models for disease prediction are of great value for the advancement of neuroscience.

Disease Prediction

Subgraph Federated Learning with Missing Neighbor Generation

1 code implementation NeurIPS 2021 Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu

Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions.

Federated Learning Graph Mining

FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis

no code implementations11 May 2021 Baihe Huang, Xiaoxiao Li, Zhao Song, Xin Yang

Nevertheless, training analysis of neural networks in FL is non-trivial for two reasons: first, the objective loss function we are optimizing is non-smooth and non-convex, and second, we are even not updating in the gradient direction.

Federated Learning

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity

no code implementations15 Apr 2021 Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, Pamela Ventola, James S. Duncan

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain.

Time Series Time Series Analysis

Estimating and Improving Fairness with Adversarial Learning

1 code implementation7 Mar 2021 Xiaoxiao Li, Ziteng Cui, Yifan Wu, Lin Gu, Tatsuya Harada

To tackle this issue, we propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system.


FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

4 code implementations ICLR 2021 Xiaoxiao Li, Meirui Jiang, Xiaofei Zhang, Michael Kamp, Qi Dou

The emerging paradigm of federated learning (FL) strives to enable collaborative training of deep models on the network edge without centrally aggregating raw data and hence improving data privacy.

Autonomous Driving Federated Learning

What Can Phase Retrieval Tell Us About Private Distributed Learning?

no code implementations ICLR 2021 Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo

In this work, we examine the security of InstaHide, a scheme recently proposed by \cite{hsla20} for preserving the security of private datasets in the context of distributed learning.


Automating Document Classification with Distant Supervision to Increase the Efficiency of Systematic Reviews

no code implementations9 Dec 2020 Xiaoxiao Li, Rabah Al-Zaidy, Amy Zhang, Stefan Baral, Le Bao, C. Lee Giles

Conclusions: In sum, the automated procedure of document classification presented here could improve both the precision and efficiency of systematic reviews, as well as facilitating live reviews, where reviews are updated regularly.

Document Classification General Classification

On InstaHide, Phase Retrieval, and Sparse Matrix Factorization

no code implementations23 Nov 2020 Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo

In this work, we examine the security of InstaHide, a scheme recently proposed by [Huang, Song, Li and Arora, ICML'20] for preserving the security of private datasets in the context of distributed learning.


MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery

no code implementations22 Oct 2020 Xiaoxiao Li, Yangsibo Huang, Binghui Peng, Zhao Song, Kai Li

To address the issue that deep neural networks (DNNs) are vulnerable to model inversion attacks, we design an objective function, which adjusts the separability of the hidden data representations, as a way to control the trade-off between data utility and vulnerability to inversion attacks.

Texture Memory-Augmented Deep Patch-Based Image Inpainting

1 code implementation28 Sep 2020 Rui Xu, Minghao Guo, Jiaqi Wang, Xiaoxiao Li, Bolei Zhou, Chen Change Loy

By bringing together the best of both paradigms, we propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions.

Image Inpainting Retrieval +1

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

no code implementations29 Jul 2020 Xiaoxiao Li, Yuan Zhou, Nicha C. Dvornek, Muhan Zhang, Juntang Zhuang, Pamela Ventola, James S. Duncan

We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders.

Knowledge Distillation Meets Self-Supervision

2 code implementations ECCV 2020 Guodong Xu, Ziwei Liu, Xiaoxiao Li, Chen Change Loy

Knowledge distillation, which involves extracting the "dark knowledge" from a teacher network to guide the learning of a student network, has emerged as an important technique for model compression and transfer learning.

Contrastive Learning Knowledge Distillation +2

Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODE

2 code implementations ICML 2020 Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, Sekhar Tatikonda, Xenophon Papademetris, James Duncan

Neural ordinary differential equations (NODEs) have recently attracted increasing attention; however, their empirical performance on benchmark tasks (e. g. image classification) are significantly inferior to discrete-layer models.

General Classification Image Classification +2

On the number of frequency hypercubes $F^n(4;2,2)$

no code implementations21 May 2020 Minjia Shi, Shukai Wang, Xiaoxiao Li, Denis S. Krotov

A frequency $n$-cube $F^n(4;2, 2)$ is an $n$-dimensional $4$-by-...-by-$4$ array filled by $0$s and $1$s such that each line contains exactly two $1$s.

Combinatorics Discrete Mathematics 05B15

Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement

1 code implementation15 May 2020 Xiaoxiao Li, Xiaopeng Guo, Liye Mei, Mingyu Shang, Jie Gao, Maojing Shu, Xiang Wang

The core of VP model is to decompose the light source into light intensity and light spatial distribution to describe the perception process of HVS, offering refinement estimation of illumination and reflectance.

Low-Light Image Enhancement

Incremental Learning for End-to-End Automatic Speech Recognition

no code implementations11 May 2020 Li Fu, Xiaoxiao Li, Libo Zi, Zhengchen Zhang, Youzheng Wu, Xiaodong He, BoWen Zhou

In this paper, we propose an incremental learning method for end-to-end Automatic Speech Recognition (ASR) which enables an ASR system to perform well on new tasks while maintaining the performance on its originally learned ones.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Research on Modeling Units of Transformer Transducer for Mandarin Speech Recognition

no code implementations26 Apr 2020 Li Fu, Xiaoxiao Li, Libo Zi

To improve the performance of RNN-T for Mandarin speech recognition task, a novel transformer transducer with the combination architecture of self-attention transformer and RNN is proposed.

speech-recognition Speech Recognition

Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification

no code implementations2 Feb 2020 Xiaoxiao Li, Joao Saude

GNNs combine node features, connection patterns, and graph structure by using a neural network to embed node information and pass it through edges in the graph.

General Classification Node Classification

Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results

1 code implementation16 Jan 2020 Xiaoxiao Li, Yufeng Gu, Nicha Dvornek, Lawrence Staib, Pamela Ventola, James S. Duncan

However, to effectively train a high-quality deep learning model, the aggregation of a significant amount of patient information is required.

Domain Adaptation Federated Learning +2

Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI

no code implementations15 Oct 2019 Nicha C. Dvornek, Xiaoxiao Li, Juntang Zhuang, James S. Duncan

The addition of the generative model constrains the network to learn functional communities represented by the LSTM nodes that are both consistent with the data generation as well as useful for the classification task.

Classification General Classification +2

Decision Explanation and Feature Importance for Invertible Networks

1 code implementation30 Sep 2019 Juntang Zhuang, Nicha C. Dvornek, Xiaoxiao Li, Junlin Yang, James S. Duncan

We can determine the decision boundary of a linear classifier in the feature space; since the transform is invertible, we can invert the decision boundary from the feature space to the input space.

Feature Importance

Ordinary differential equations on graph networks

no code implementations25 Sep 2019 Juntang Zhuang, Nicha Dvornek, Xiaoxiao Li, James S. Duncan

Inspired by neural ordinary differential equation (NODE) for data in the Euclidean domain, we extend the idea of continuous-depth models to graph data, and propose graph ordinary differential equation (GODE).

Graph Classification Node Classification

Graph Embedding Using Infomax for ASD Classification and Brain Functional Difference Detection

no code implementations9 Aug 2019 Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang, Pamela Ventola, James Duncan

Here, we model the whole brain fMRI as a graph, which preserves geometrical and temporal information and use a Graph Neural Network (GNN) to learn from the graph-structured fMRI data.

Classification General Classification +1

Deep Flow-Guided Video Inpainting

2 code implementations CVPR 2019 Rui Xu, Xiaoxiao Li, Bolei Zhou, Chen Change Loy

Then the synthesized flow field is used to guide the propagation of pixels to fill up the missing regions in the video.

One-shot visual object segmentation Optical Flow Estimation +2

Hybrid Task Cascade for Instance Segmentation

5 code implementations CVPR 2019 Kai Chen, Jiangmiao Pang, Jiaqi Wang, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin

In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.

Instance Segmentation object-detection +3

Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery

no code implementations14 Dec 2018 Xiaoxiao Li, Nicha C. Dvornek, Yuan Zhou, Juntang Zhuang, Pamela Ventola, James S. Duncan

Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models.

Feature Importance

Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging

no code implementations8 Nov 2018 Xiaoxiao Li, Vivek Singh, Yifan Wu, Klaus Kirchberg, James Duncan, Ankur Kapoor

Tracking organ motion is important in image-guided interventions, but motion annotations are not always easily available.

Motion Estimation

Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI

no code implementations23 Aug 2018 Xiaoxiao Li, Nicha C. Dvornek, Juntang Zhuang, Pamela Ventola, James S. Duncan

Therefore, in this work, we address the problem of interpreting reliable biomarkers associated with identifying ASD; specifically, we propose a 2-stage method that classifies ASD and control subjects using fMRI images and interprets the saliency features activated by the classifier.

Decision Making

Video Object Segmentation with Re-identification

3 code implementations1 Aug 2017 Xiaoxiao Li, Yuankai Qi, Zhe Wang, Kai Chen, Ziwei Liu, Jianping Shi, Ping Luo, Xiaoou Tang, Chen Change Loy

Specifically, our Video Object Segmentation with Re-identification (VS-ReID) model includes a mask propagation module and a ReID module.

Semantic Segmentation Video Object Segmentation +2

Semantic Image Segmentation via Deep Parsing Network

no code implementations ICCV 2015 Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang

This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts.

Image Segmentation Semantic Segmentation

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