Search Results for author: Gang Li

Found 96 papers, 29 papers with code

FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 Classes

no code implementations28 Feb 2024 Ziying Pan, Kun Wang, Gang Li, Feihong He, Xiwang Li, Yongxuan Lai

The class-conditional image generation based on diffusion models is renowned for generating high-quality and diverse images.

Conditional Image Generation

FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models

no code implementations28 Jan 2024 Feihong He, Gang Li, Mengyuan Zhang, Leilei Yan, Lingyu Si, Fanzhang Li

In the decoder, we further modulate features from the dual streams based on a given content image and the corresponding style text prompt for precise style transfer.

Style Transfer

Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation

no code implementations11 Jan 2024 Seung Hyun Lee, Yinxiao Li, Junjie Ke, Innfarn Yoo, Han Zhang, Jiahui Yu, Qifei Wang, Fei Deng, Glenn Entis, Junfeng He, Gang Li, Sangpil Kim, Irfan Essa, Feng Yang

Additionally, Parrot employs a joint optimization approach for the T2I model and the prompt expansion network, facilitating the generation of quality-aware text prompts, thus further enhancing the final image quality.

Reinforcement Learning (RL) Text-to-Image Generation

Predicting Infant Brain Connectivity with Federated Multi-Trajectory GNNs using Scarce Data

1 code implementation1 Jan 2024 Michalis Pistos, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik

The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations.

Federated Learning Imputation +1

Rich Human Feedback for Text-to-Image Generation

no code implementations15 Dec 2023 Youwei Liang, Junfeng He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, Junjie Ke, Krishnamurthy Dj Dvijotham, Katie Collins, Yiwen Luo, Yang Li, Kai J Kohlhoff, Deepak Ramachandran, Vidhya Navalpakkam

We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.

Text-to-Image Generation

UniAR: Unifying Human Attention and Response Prediction on Visual Content

no code implementations15 Dec 2023 Peizhao Li, Junfeng He, Gang Li, Rachit Bhargava, Shaolei Shen, Nachiappan Valliappan, Youwei Liang, Hongxiang Gu, Venky Ramachandran, Golnaz Farhadi, Yang Li, Kai J Kohlhoff, Vidhya Navalpakkam

Such a model would enable predicting subjective feedback such as overall satisfaction or aesthetic quality ratings, along with the underlying human attention or interaction heatmaps and viewing order, enabling designers and content-creation models to optimize their creation for human-centric improvements.

AUC-mixup: Deep AUC Maximization with Mixup

no code implementations18 Oct 2023 Jianzhi Xv, Gang Li, Tianbao Yang

While deep AUC maximization (DAM) has shown remarkable success on imbalanced medical tasks, e. g., chest X-rays classification and skin lesions classification, it could suffer from severe overfitting when applied to small datasets due to its aggressive nature of pushing prediction scores of positive data away from that of negative data.

Data Augmentation

Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model

no code implementations16 Oct 2023 Junpeng Tan, Xin Zhang, Yao Lv, Xiangmin Xu, Gang Li

Finally, the experimental results on real-world fetal brain MRI stacks demonstrate the state-of-the-art performance of our method.

3D Reconstruction Super-Resolution

A Zero-Shot Language Agent for Computer Control with Structured Reflection

no code implementations12 Oct 2023 Tao Li, Gang Li, Zhiwei Deng, Bryan Wang, Yang Li

To perform a task, recent works often require a model to learn from trace examples of the task via either supervised learning or few/many-shot prompting.

Management

Automatic Macro Mining from Interaction Traces at Scale

no code implementations10 Oct 2023 Forrest Huang, Gang Li, Tao Li, Yang Li

Macros are building block tasks of our everyday smartphone activity (e. g., "login", or "booking a flight").

PrototypeFormer: Learning to Explore Prototype Relationships for Few-shot Image Classification

no code implementations5 Oct 2023 Feihong He, Gang Li, Lingyu Si, Leilei Yan, Fanzhang Li, Fuchun Sun

In particular, our method achieves 97. 07% and 90. 88% on 5-way 5-shot and 5-way 1-shot tasks of miniImageNet, which surpasses the state-of-the-art results with accuracy of 7. 27% and 8. 72%, respectively.

Classification Contrastive Learning +2

Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor

no code implementations12 Jul 2023 Zhuowen Yin, Xinyao Ding, Xin Zhang, Zhengwang Wu, Li Wang, Gang Li

Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features.

Improving Federated Aggregation with Deep Unfolding Networks

no code implementations30 Jun 2023 Shanika I Nanayakkara, Shiva Raj Pokhrel, Gang Li

By incorporating unbiased weights into the model, the proposed approach effectively addresses quality-aware aggregation under the heterogeneity of the participating clients and the FL environment.

Federated Learning

Quantum Federated Learning: Analysis, Design and Implementation Challenges

no code implementations27 Jun 2023 Dev Gurung, Shiva Raj Pokhrel, Gang Li

Quantum Federated Learning (QFL) has gained significant attention due to quantum computing and machine learning advancements.

Federated Learning

Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation

1 code implementation20 Jun 2023 Dev Gurung, Shiva Raj Pokhrel, Gang Li

To this end, we develop a decentralized and trustworthy quantum federated learning (QFL) framework.

Federated Learning

Artificial General Intelligence for Medical Imaging

no code implementations8 Jun 2023 Xiang Li, Lu Zhang, Zihao Wu, Zhengliang Liu, Lin Zhao, Yixuan Yuan, Jun Liu, Gang Li, Dajiang Zhu, Pingkun Yan, Quanzheng Li, Wei Liu, Tianming Liu, Dinggang Shen

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models.

LibAUC: A Deep Learning Library for X-Risk Optimization

1 code implementation5 Jun 2023 Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Xuanhui Wang, Tianbao Yang

This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-risks.

Benchmarking Classification +2

Do We Need an Encoder-Decoder to Model Dynamical Systems on Networks?

no code implementations20 May 2023 Bing Liu, Wei Luo, Gang Li, Jing Huang, Bo Yang

As deep learning gains popularity in modelling dynamical systems, we expose an underappreciated misunderstanding relevant to modelling dynamics on networks.

Time Series

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task

no code implementations18 Apr 2023 Zihao Wu, Lu Zhang, Chao Cao, Xiaowei Yu, Haixing Dai, Chong Ma, Zhengliang Liu, Lin Zhao, Gang Li, Wei Liu, Quanzheng Li, Dinggang Shen, Xiang Li, Dajiang Zhu, Tianming Liu

To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned specifically on task-related data samples.

Specificity Task 2

IterativePFN: True Iterative Point Cloud Filtering

1 code implementation CVPR 2023 Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao, Gang Li, Antonio Robles-Kelly, Ying He

Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud filtering or denoising.

Denoising

COMIC: An Unsupervised Change Detection Method for Heterogeneous Remote Sensing Images Based on Copula Mixtures and Cycle-Consistent Adversarial Networks

no code implementations3 Apr 2023 Chengxi Li, Gang Li, Zhuoyue Wang, Xueqian Wang, Pramod K. Varshney

For this problem, an unsupervised change detection method has been proposed recently based on the image translation technique of Cycle-Consistent Adversarial Networks (CycleGANs), where one image is translated from its original modality to the modality of the other image so that the difference map can be obtained by performing arithmetical subtraction.

Change Detection Translation

Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading

no code implementations30 Mar 2023 Minglei Lu, Ali Mohammadi, Zhaoxu Meng, Xuhui Meng, Gang Li, Zhen Li

After an offline training, the DNO model can act as surrogate of physics-based FEA to predict the transient mechanical response in terms of reaction force and stress distribution of the IPCs to various strain loads in one second at an accuracy of 98%.

Incremental Learning

Ins-ATP: Deep Estimation of ATP for Organoid Based on High Throughput Microscopic Images

1 code implementation13 Mar 2023 Xuesheng Bian, Cheng Wang, Shuting Chen, Weiquan Liu, Sen Xu, Jinxin Zhu, Rugang Wang, Zexin Chen, Min Huang, Gang Li

Performing ATP bioluminescence causes cell lysis of organoids, so it is impossible to observe organoids' long-term viability changes after medication continually.

Post Quantum Secure Blockchain-based Federated Learning for Mobile Edge Computing

1 code implementation26 Feb 2023 Rongxin Xu, Shiva Raj Pokhrel, Qiujun Lan, Gang Li

We aim to employ Federated Learning (FL) and prominent features of blockchain into MEC architecture such as connected autonomous vehicles to enable complete decentralization, immutability, and rewarding mechanisms simultaneously.

Autonomous Vehicles Edge-computing +1

$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks

1 code implementation1 Feb 2023 Zeyu Zhu, Fanrong Li, Zitao Mo, Qinghao Hu, Gang Li, Zejian Liu, Xiaoyao Liang, Jian Cheng

Through an in-depth analysis of the topology of GNNs, we observe that the topology of the graph leads to significant differences between nodes, and most of the nodes in a graph appear to have a small aggregation value.

Quantization

PLay: Parametrically Conditioned Layout Generation using Latent Diffusion

no code implementations27 Jan 2023 Chin-Yi Cheng, Forrest Huang, Gang Li, Yang Li

Layout design is an important task in various design fields, including user interface, document, and graphic design.

Layout Design

Detecting Change Intervals with Isolation Distributional Kernel

2 code implementations30 Dec 2022 Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li

Detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis.

Change Point Detection

MCTNet: A Multi-Scale CNN-Transformer Network for Change Detection in Optical Remote Sensing Images

no code implementations14 Oct 2022 Weiming Li, Lihui Xue, Xueqian Wang, Gang Li

For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction.

Change Detection

MUG: Interactive Multimodal Grounding on User Interfaces

no code implementations29 Sep 2022 Tao Li, Gang Li, Jingjie Zheng, Purple Wang, Yang Li

To investigate the problem, we create a new dataset that consists of 77, 820 sequences of human user-agent interaction on mobile interfaces in which 20% involves multiple rounds of interactions.

Spotlight: Mobile UI Understanding using Vision-Language Models with a Focus

no code implementations29 Sep 2022 Gang Li, Yang Li

Specifically, we enhance a vision-language model that only takes the screenshot of the UI and a region of interest on the screen -- the focus -- as the input.

Language Modelling Multi-Task Learning

Enabling Conversational Interaction with Mobile UI using Large Language Models

1 code implementation18 Sep 2022 Bryan Wang, Gang Li, Yang Li

This paper investigates the feasibility of enabling versatile conversational interactions with mobile UIs using a single LLM.

Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering

1 code implementation14 Aug 2022 Dasith de Silva Edirimuni, Xuequan Lu, Gang Li, Antonio Robles-Kelly

Existing methods usually perform normal estimation and filtering separately and often show sensitivity to noise and/or inability to preserve sharp geometric features such as corners and edges.

Contrastive Learning

PalQuant: Accelerating High-precision Networks on Low-precision Accelerators

1 code implementation3 Aug 2022 Qinghao Hu, Gang Li, Qiman Wu, Jian Cheng

In this paper, we propose the PArallel Low-precision Quantization (PalQuant) method that approximates high-precision computations via learning parallel low-precision representations from scratch.

Quantization Vocal Bursts Intensity Prediction

Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization

no code implementations18 Jul 2022 Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang, Tianbao Yang

The key issue is to track and estimate a sequence of $\mathbf g(\mathbf{w})=(g_1(\mathbf{w}), \ldots, g_m(\mathbf{w}))$ across iterations, where $\mathbf g(\mathbf{w})$ has $m$ blocks and it is only allowed to probe $\mathcal{O}(1)$ blocks to attain their stochastic values and Jacobians.

DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection

1 code implementation12 Jul 2022 Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang

Specifically, we propose the Inverse NMS Clustering (INC) and Rank Matching (RM) to instantiate the dense supervision, without the widely used, conventional sparse pseudo labels.

object-detection Object Detection +1

FAIR-BFL: Flexible and Incentive Redesign for Blockchain-based Federated Learning

no code implementations26 Jun 2022 Rongxin Xu, Shiva Raj Pokhrel, Qiujun Lan, Gang Li

These impending challenges in the design philosophy of FL call for blockchain-based federated learning (BFL) due to the benefits of coupling FL and blockchain (e. g., democracy, incentive, and immutability).

Federated Learning Malicious Detection +1

SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders

1 code implementation21 Jun 2022 Gang Li, Heliang Zheng, Daqing Liu, Chaoyue Wang, Bing Su, Changwen Zheng

In this paper, we explore a potential visual analogue of words, i. e., semantic parts, and we integrate semantic information into the training process of MAE by proposing a Semantic-Guided Masking strategy.

Language Modelling Masked Language Modeling +1

Representing Brain Anatomical Regularity and Variability by Few-Shot Embedding

no code implementations26 May 2022 Lu Zhang, Xiaowei Yu, Yanjun Lyu, Zhengwang Wu, Haixing Dai, Lin Zhao, Li Wang, Gang Li, Tianming Liu, Dajiang Zhu

Our experimental results show that: 1) the learned embedding vectors can quantitatively encode the commonality and individuality of cortical folding patterns; 2) with the embeddings we can robustly infer the complicated many-to-many anatomical correspondences among different brains and 3) our model can be successfully transferred to new populations with very limited training samples.

Few-Shot Learning

Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis

1 code implementation5 Apr 2022 Eldon Schoop, Xin Zhou, Gang Li, Zhourong Chen, Björn Hartmann, Yang Li

We use a deep learning based approach to predict whether a selected element in a mobile UI screenshot will be perceived by users as tappable, based on pixels only instead of view hierarchies required by previous work.

PseCo: Pseudo Labeling and Consistency Training for Semi-Supervised Object Detection

1 code implementation30 Mar 2022 Gang Li, Xiang Li, Yujie Wang, Yichao Wu, Ding Liang, Shanshan Zhang

Specifically, for pseudo labeling, existing works only focus on the classification score yet fail to guarantee the localization precision of pseudo boxes; For consistency training, the widely adopted random-resize training only considers the label-level consistency but misses the feature-level one, which also plays an important role in ensuring the scale invariance.

object-detection Object Detection +1

A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis

no code implementations29 Mar 2022 Guangwei Yu, Gang Li, Xingtong Si, Zhuoyuan Song

Ball mixing is a ball bearing production quality problem that is difficult to identify using traditional frequency domain analysis methods since it requires high frequency resolutions of the measurement signals and results in a long analyzing time.

When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee

no code implementations1 Mar 2022 Dixian Zhu, Gang Li, Bokun Wang, Xiaodong Wu, Tianbao Yang

In this paper, we propose systematic and efficient gradient-based methods for both one-way and two-way partial AUC (pAUC) maximization that are applicable to deep learning.

Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning

1 code implementation28 Jan 2022 Jie Zhang, Lei Zhang, Gang Li, Chao Wu

Adversarial examples are inputs for machine learning models that have been designed by attackers to cause the model to make mistakes.

Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale

1 code implementation11 Jan 2022 Gang Li, Gilles Baechler, Manuel Tragut, Yang Li

The layout of a mobile screen is a critical data source for UI design research and semantic understanding of the screen.

Denoising valid

SimViT: Exploring a Simple Vision Transformer with sliding windows

2 code implementations24 Dec 2021 Gang Li, Di Xu, Xing Cheng, Lingyu Si, Changwen Zheng

Although vision Transformers have achieved excellent performance as backbone models in many vision tasks, most of them intend to capture global relations of all tokens in an image or a window, which disrupts the inherent spatial and local correlations between patches in 2D structure.

VUT: Versatile UI Transformer for Multi-Modal Multi-Task User Interface Modeling

no code implementations10 Dec 2021 Yang Li, Gang Li, Xin Zhou, Mostafa Dehghani, Alexey Gritsenko

Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input.

object-detection Object Detection +2

Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation

no code implementations9 Dec 2021 Gang Li, Xiang Li, Yujie Wang, Shanshan Zhang, Yichao Wu, Ding Liang

Based on the two observations, we propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors, respectively.

Image Classification Knowledge Distillation +3

Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models

no code implementations14 Oct 2021 Forrest Huang, Gang Li, Xin Zhou, John F. Canny, Yang Li

The design process of user interfaces (UIs) often begins with articulating high-level design goals.

Retrieval

A Few-shot Learning Graph Multi-Trajectory Evolution Network for Forecasting Multimodal Baby Connectivity Development from a Baseline Timepoint

1 code implementation6 Oct 2021 Alaa Bessadok, Ahmed Nebli, Mohamed Ali Mahjoub, Gang Li, Weili Lin, Dinggang Shen, Islem Rekik

To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs).

Few-Shot Learning Trajectory Prediction

VUT: Versatile UI Transformer for Multimodal Multi-Task User Interface Modeling

no code implementations29 Sep 2021 Yang Li, Gang Li, Xin Zhou, Mostafa Dehghani, Alexey A. Gritsenko

Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input.

object-detection Object Detection +2

Screen2Words: Automatic Mobile UI Summarization with Multimodal Learning

2 code implementations7 Aug 2021 Bryan Wang, Gang Li, Xin Zhou, Zhourong Chen, Tovi Grossman, Yang Li

Mobile User Interface Summarization generates succinct language descriptions of mobile screens for conveying important contents and functionalities of the screen, which can be useful for many language-based application scenarios.

HelpViz: Automatic Generation of Contextual Visual MobileTutorials from Text-Based Instructions

no code implementations7 Aug 2021 Mingyuan Zhong, Gang Li, Peggy Chi, Yang Li

We present HelpViz, a tool for generating contextual visual mobile tutorials from text-based instructions that are abundant on the web.

Gate-Tunable Optical Extinction of Graphene Nanoribbon Nanoclusters

no code implementations12 Mar 2021 Erin Sheridan, Gang Li, Mamun Sarker, Shan Hao, Ki-Tae Eom, Chang-Beom Eom, Alexander Sinitskii, Patrick Irvin, Jeremy Levy

We investigate the optical response of graphene nanoribbons (GNRs) using the broadband nonlinear generation and detection capabilities of nanoscale junctions created at the LaAlO$_3$/SrTiO$_3$ interface.

Mesoscale and Nanoscale Physics Optics

Hardware Acceleration of Fully Quantized BERT for Efficient Natural Language Processing

no code implementations4 Mar 2021 Zejian Liu, Gang Li, Jian Cheng

BERT is the most recent Transformer-based model that achieves state-of-the-art performance in various NLP tasks.

Edge-computing

Pressure-induced Superconductivity in dual-topological semimetal Pt2HgSe3

no code implementations17 Feb 2021 Cuiying Pei, Suhua Jin, Peihao Huang, Anna Vymazalova, Lingling Gao, Yi Zhao, Weizheng Cao, Changhua Li, Peter Nemes-Incze, Yulin Chen, Hanyu Liu, Gang Li, Yanpeng Qi

Recently monolayer jacutingaite (Pt2HgSe3), a naturally occurring exfoliable mineral, discovered in Brazil in 2008, has been theoretically predicted as a candidate quantum spin Hall system with a 0. 5 eV band gap, while the bulk form is one of only a few known dual-topological insulators which may host different surface states protected by symmetries.

Band Gap Superconductivity Materials Science

Spacewalker: Rapid UI Design Exploration Using Lightweight Markup Enhancement and Crowd Genetic Programming

no code implementations17 Feb 2021 Mingyuan Zhong, Gang Li, Yang Li

Based on our experiments, Spacewalker allows designers to effectively search a large design space of a UI, using the language they are familiar with, and improve their design rapidly at a minimal cost.

Attribute

Dynamic Dual Gating Neural Networks

1 code implementation ICCV 2021 Fanrong Li, Gang Li, Xiangyu He, Jian Cheng

In particular, dynamic dual gating can provide 59. 7% saving in computing of ResNet50 with 76. 41% top-1 accuracy on ImageNet, which has advanced the state-of-the-art.

Decentralized Federated Learning via Mutual Knowledge Transfer

no code implementations24 Dec 2020 Chengxi Li, Gang Li, Pramod K. Varshney

In this paper, we investigate the problem of decentralized federated learning (DFL) in Internet of things (IoT) systems, where a number of IoT clients train models collectively for a common task without sharing their private training data in the absence of a central server.

Federated Learning Transfer Learning

The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms

no code implementations12 Oct 2020 Xin Han, Ye Zhu, Kai Ming Ting, Gang Li

In this paper, we identify the root cause of this issue and show that the use of a data-dependent kernel (instead of distance or existing kernel) provides an effective means to address it.

Clustering

Widget Captioning: Generating Natural Language Description for Mobile User Interface Elements

1 code implementation EMNLP 2020 Yang Li, Gang Li, Luheng He, Jingjie Zheng, Hong Li, Zhiwei Guan

We propose widget captioning, a novel task for automatically generating language descriptions for UI elements from multimodal input including both the image and the structural representations of user interfaces.

Deep Modeling of Growth Trajectories for Longitudinal Prediction of Missing Infant Cortical Surfaces

1 code implementation6 Sep 2020 Peirong Liu, Zhengwang Wu, Gang Li, Pew-Thian Yap, Dinggang Shen

Charting cortical growth trajectories is of paramount importance for understanding brain development.

Deep Fiducial Inference

no code implementations8 Jul 2020 Gang Li, Jan Hannig

Since the mid-2000s, there has been a resurrection of interest in modern modifications of fiducial inference.

Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge

no code implementations4 Jul 2020 Yue Sun, Kun Gao, Zhengwang Wu, Zhihao Lei, Ying WEI, Jun Ma, Xiaoping Yang, Xue Feng, Li Zhao, Trung Le Phan, Jitae Shin, Tao Zhong, Yu Zhang, Lequan Yu, Caizi Li, Ramesh Basnet, M. Omair Ahmad, M. N. S. Swamy, Wenao Ma, Qi Dou, Toan Duc Bui, Camilo Bermudez Noguera, Bennett Landman, Ian H. Gotlib, Kathryn L. Humphreys, Sarah Shultz, Longchuan Li, Sijie Niu, Weili Lin, Valerie Jewells, Gang Li, Dinggang Shen, Li Wang

Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.

Brain Segmentation

Change Detection in Heterogeneous Optical and SAR Remote Sensing Images via Deep Homogeneous Feature Fusion

no code implementations8 Apr 2020 Xiao Jiang, Gang Li, Yu Liu, Xiao-Ping Zhang, You He

To solve this problem, this paper presents a new homogeneous transformation model termed deep homogeneous feature fusion (DHFF) based on image style transfer (IST).

Change Detection Style Transfer

TopoAna: A generic tool for the event type analysis of inclusive Monte-Carlo samples in high energy physics experiments

1 code implementation13 Jan 2020 Xingyu Zhou, Shuxian Du, Gang Li, Chengping Shen

To help analysts obtain the physics process information from the truth information of the samples, we develop a physics process analysis program, TopoAna, with C++, ROOT, and LaTeX.

High Energy Physics - Experiment

Performance of regression models as a function of experiment noise

2 code implementations17 Dec 2019 Gang Li, Jan Zrimec, Boyang Ji, Jun Geng, Johan Larsbrink, Aleksej Zelezniak, Jens Nielsen, Martin KM Engqvist

A challenge in developing machine learning regression models is that it is difficult to know whether maximal performance has been reached on a particular dataset, or whether further model improvement is possible.

regression

A System-Level Solution for Low-Power Object Detection

no code implementations24 Sep 2019 Fanrong Li, Zitao Mo, Peisong Wang, Zejian Liu, Jiayun Zhang, Gang Li, Qinghao Hu, Xiangyu He, Cong Leng, Yang Zhang, Jian Cheng

As a case study, we evaluate our object detection system on a real-world surveillance video with input size of 512x512, and it turns out that the system can achieve an inference speed of 18 fps at the cost of 6. 9W (with display) with an mAP of 66. 4 verified on the PASCAL VOC 2012 dataset.

Object object-detection +2

Learning to Infer Entities, Properties and their Relations from Clinical Conversations

no code implementations IJCNLP 2019 Nan Du, Mingqiu Wang, Linh Tran, Gang Li, Izhak Shafran

Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e. g., symptoms) and their properties (e. g., duration).

Attribute Relation Extraction

Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack

no code implementations9 May 2019 Di Zhao, Jiqiang Liu, Jialin Wang, Wenjia Niu, Endong Tong, Tong Chen, Gang Li

"Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain.

Attribute Clustering

Spherical U-Net on Cortical Surfaces: Methods and Applications

no code implementations1 Apr 2019 Fenqiang Zhao, Shunren Xia, Zhengwang Wu, Dingna Duan, Li Wang, Weili Lin, John H Gilmore, Dinggang Shen, Gang Li

In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid.

Attribute Computational Efficiency

Training Binary Weight Networks via Semi-Binary Decomposition

no code implementations ECCV 2018 Qinghao Hu, Gang Li, Peisong Wang, Yifan Zhang, Jian Cheng

In this paper, we propose a novel semi-binary decomposition method which decomposes a matrix into two binary matrices and a diagonal matrix.

Computational Efficiency

Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding

no code implementations18 Jul 2018 Tong Chen, Wenjia Niu, Yingxiao Xiang, Xiaoxuan Bai, Jiqiang Liu, Zhen Han, Gang Li

In addition, we propose Gradient Band-based Adversarial Training, which trained with a single randomly choose dominant adversarial example without taking any modification, to realize the "1:N" attack immunity for generalized dominant adversarial examples.

Recent Advances in Efficient Computation of Deep Convolutional Neural Networks

no code implementations3 Feb 2018 Jian Cheng, Peisong Wang, Gang Li, Qinghao Hu, Hanqing Lu

As for hardware implementation of deep neural networks, a batch of accelerators based on FPGA/ASIC have been proposed in recent years.

Network Pruning Quantization

Scale-constrained Unsupervised Evaluation Method for Multi-scale Image Segmentation

1 code implementation15 Nov 2016 Yuhang Lu, Youchuan Wan, Gang Li

Unsupervised evaluation of segmentation quality is a crucial step in image segmentation applications.

Image Segmentation Segmentation +1

Efficient Regularized Regression for Variable Selection with L0 Penalty

no code implementations28 Jul 2014 Zhenqiu Liu, Gang Li

Therefore, it is natural to expect that L0 regularized regression performs better than LASSO.

regression Variable Selection

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