Search Results for author: Jie Ma

Found 36 papers, 9 papers with code

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning

no code implementations10 Aug 2023 Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou, Jie Ma, Patrick Ng, Zhiguo Wang, Bonan Min, William Wang, Kathleen McKeown, Vittorio Castelli, Dan Roth, Bing Xiang

We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.

Data-to-Text Generation

Robust Visual Question Answering: Datasets, Methods, and Future Challenges

no code implementations21 Jul 2023 Jie Ma, Pinghui Wang, Dechen Kong, Zewei Wang, Jun Liu, Hongbin Pei, Junzhou Zhao

Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives.

Question Answering Visual Question Answering

Taxonomy Expansion for Named Entity Recognition

no code implementations22 May 2023 Karthikeyan K, Yogarshi Vyas, Jie Ma, Giovanni Paolini, Neha Anna John, Shuai Wang, Yassine Benajiba, Vittorio Castelli, Dan Roth, Miguel Ballesteros

We experiment with 6 diverse datasets and show that PLM consistently performs better than most other approaches (0. 5 - 2. 5 F1), including in novel settings for taxonomy expansion not considered in prior work.

named-entity-recognition Named Entity Recognition +2

Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs

1 code implementation20 May 2023 Mengze Xu, Jie Ma, Yuanyuan Zhu

In our work, we introduce conditional denoising diffusion probabilistic models (DDPM) from two aspects: kernel estimation progress and re-construction progress, named as the dual-diffusion.

Blind Super-Resolution Denoising +2

Scribble-Supervised Target Extraction Method Based on Inner Structure-Constraint for Remote Sensing Images

1 code implementation18 May 2023 Yitong Li, Chang Liu, Jie Ma

Weakly supervised learning based on scribble annotations in target extraction of remote sensing images has drawn much interest due to scribbles' flexibility in denoting winding objects and low cost of manually labeling.

Weakly-supervised Learning

Weakly-supervised ROI extraction method based on contrastive learning for remote sensing images

1 code implementation10 May 2023 Lingfeng He, Mengze Xu, Jie Ma

ROI extraction is an active but challenging task in remote sensing because of the complicated landform, the complex boundaries and the requirement of annotations.

Contrastive Learning Weakly-supervised Learning

Adaptive loose optimization for robust question answering

1 code implementation6 May 2023 Jie Ma, Pinghui Wang, Zewei Wang, Dechen Kong, Min Hu, Ting Han, Jun Liu

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering).

Extractive Question-Answering Machine Reading Comprehension +2

Health Monitoring of Movement Disorder Subject based on Diamond Stacked Sparse Autoencoder Ensemble Model

no code implementations15 Mar 2023 Likun Tang, Jie Ma, Yongming Li

Machine learning-based processing of data collected from the human with movement disorders using wearable sensors is an effective method currently available for health monitoring.

Dimensionality Reduction

Enhanced Soft Label for Semi-Supervised Semantic Segmentation

no code implementations ICCV 2023 Jie Ma, Chuan Wang, Yang Liu, Liang Lin, Guanbin Li

As a mainstream framework in the field of semi-supervised learning (SSL), self-training via pseudo labeling and its variants have witnessed impressive progress in semi-supervised semantic segmentation with the recent advance of deep neural networks.

Contrastive Learning Pseudo Label +1

Context-Aware Data Augmentation for LIDAR 3D Object Detection

no code implementations20 Nov 2022 Xuzhong Hu, Zaipeng Duan, Jie Ma

For 3D object detection, labeling lidar point cloud is difficult, so data augmentation is an important module to make full use of precious annotated data.

3D Object Detection Data Augmentation +1

A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model

no code implementations25 Oct 2022 Chuanyan Zhou, Jie Ma, Fan Li, Yongming Li, Pin Wang, Xiaoheng Zhang

Second, an embedded stack autoencoder (ESAE) is proposed and trained in each layer of sample space to consider the original samples during training and in the network structure, thereby better finding the relationship between original feature samples and deep feature samples.

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

2 code implementations21 Sep 2022 Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang

To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch.

Contrastive Learning Federated Learning

Visualizing and Understanding Patch Interactions in Vision Transformer

no code implementations11 Mar 2022 Jie Ma, Yalong Bai, Bineng Zhong, Wei zhang, Ting Yao, Tao Mei

Vision Transformer (ViT) has become a leading tool in various computer vision tasks, owing to its unique self-attention mechanism that learns visual representations explicitly through cross-patch information interactions.

On the Convergence of Clustered Federated Learning

1 code implementation13 Feb 2022 Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang

Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL).

Federated Learning

Pareto Policy Pool for Model-based Offline Reinforcement Learning

no code implementations ICLR 2022 Yijun Yang, Jing Jiang, Tianyi Zhou, Jie Ma, Yuhui Shi

Model-based offline RL instead trains an environment model using a dataset of pre-collected experiences so online RL methods can learn in an offline manner by solely interacting with the model.

D4RL Offline RL +2

Leveraging Large-Scale Weakly Labeled Data for Semi-Supervised Mass Detection in Mammograms

no code implementations CVPR 2021 Yuxing Tang, Zhenjie Cao, Yanbo Zhang, Zhicheng Yang, Zongcheng Ji, Yiwei Wang, Mei Han, Jie Ma, Jing Xiao, Peng Chang

Starting with a fully supervised model trained on the data with pixel-level masks, the proposed framework iteratively refines the model itself using the entire weakly labeled data (image-level soft label) in a self-training fashion.

DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis

no code implementations12 Jun 2021 Yujiao Wu, Jie Ma, Xiaoshui Huang, Sai Ho Ling, Steven Weidong Su

To improve the survival prediction accuracy and help prognostic decision-making in clinical practice for medical experts, we for the first time propose a multimodal deep learning method for non-small cell lung cancer (NSCLC) survival analysis, named DeepMMSA.

Decision Making Multimodal Deep Learning +2

Extremal problems of Erdős, Faudree, Schelp and Simonovits on paths and cycles

no code implementations8 Feb 2021 Binlong Li, Jie Ma, Bo Ning

Many years ago, Erd\H{o}s, Faudree, Schelp and Simonovits proposed the study of the function $\phi(n, d, k)$, and conjectured that for any positive integers $n>d\geq k$, it holds that $\phi(n, d, k)\leq \lfloor\frac{k-1}{2}\rfloor\lfloor\frac{n}{d+1}\rfloor+\epsilon$, where $\epsilon=1$ if $k$ is odd and $\epsilon=2$ otherwise.


Structured Prediction as Translation between Augmented Natural Languages

1 code implementation ICLR 2021 Giovanni Paolini, Ben Athiwaratkun, Jason Krone, Jie Ma, Alessandro Achille, Rishita Anubhai, Cicero Nogueira dos santos, Bing Xiang, Stefano Soatto

We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extraction, nested named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, and dialogue state tracking.

coreference-resolution Dialogue State Tracking +10

Improvements on induced subgraphs of given sizes

no code implementations11 Jan 2021 Jialin He, Jie Ma, Lilu Zhao

al. Our second result considers infinitely many pairs $(m, f)$ of special forms, whose exact values of $\sigma(m, f)$ were conjectured by Erd\H{o}s et.


When and Who? Conversation Transition Based on Bot-Agent Symbiosis Learning Network

no code implementations COLING 2020 Yipeng Yu, Ran Guan, Jie Ma, Zhuoxuan Jiang, Jingchang Huang

In online customer service applications, multiple chatbots that are specialized in various topics are typically developed separately and are then merged with other human agents to a single platform, presenting to the users with a unified interface.

XTQA: Span-Level Explanations of the Textbook Question Answering

1 code implementation25 Nov 2020 Jie Ma, Qi Chai, Jun Liu, Qingyu Yin, Pinghui Wang, Qinghua Zheng

Textbook Question Answering (TQA) is a task that one should answer a diagram/non-diagram question given a large multi-modal context consisting of abundant essays and diagrams.

Question Answering

To BERT or Not to BERT: Comparing Task-specific and Task-agnostic Semi-Supervised Approaches for Sequence Tagging

no code implementations EMNLP 2020 Kasturi Bhattacharjee, Miguel Ballesteros, Rishita Anubhai, Smaranda Muresan, Jie Ma, Faisal Ladhak, Yaser Al-Onaizan

Leveraging large amounts of unlabeled data using Transformer-like architectures, like BERT, has gained popularity in recent times owing to their effectiveness in learning general representations that can then be further fine-tuned for downstream tasks to much success.

Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer

no code implementations27 Jun 2020 Qian Li, Qingyuan Hu, Yong Qi, Saiyu Qi, Jie Ma, Jian Zhang

SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples.

Data Augmentation

Some exact results on $4$-cycles: stability and supersaturation

no code implementations2 Dec 2019 Jialin He, Jie Ma, Tianchi Yang

A longstanding conjecture of Erd\H{o}s and Simonovits states that every $n$-vertex graph with $ex(n, C_4)+1$ edges contains at least $(1+o(1))\sqrt{n}$ 4-cycles.


Context-aware Attention Model for Coreference Resolution

no code implementations25 Sep 2019 Yufei Li, Xiangyu Zhou, Jie Ma, Yu Long, Xuan Wang, Chen Li

Coreference resolution is an important task for gaining more complete understanding about texts by artificial intelligence.


Deep joint rain and haze removal from single images

no code implementations21 Jan 2018 Liang Shen, Zihan Yue, Quan Chen, Fan Feng, Jie Ma

On the other hand, the accumulation of rain streaks from long distance makes the rain image look like haze veil.

Rain Removal

MSR-net:Low-light Image Enhancement Using Deep Convolutional Network

no code implementations7 Nov 2017 Liang Shen, Zihan Yue, Fan Feng, Quan Chen, Shihao Liu, Jie Ma

In this paper, a low-light image enhancement model based on convolutional neural network and Retinex theory is proposed.

Low-Light Image Enhancement

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