Search Results for author: Mingyang Li

Found 34 papers, 13 papers with code

面向微博文本的融合字词信息的轻量级命名实体识别(Lightweight Named Entity Recognition for Weibo Based on Word and Character)

no code implementations CCL 2020 Chun Chen, Mingyang Li, Fang Kong

中文社交媒体命名实体识别由于其领域特殊性, 一直广受关注。非正式且无结构的微博文本存在以下两个问题:一是词语边界模糊;二是语料规模有限。针对问题一, 本文将同维度的字词进行融合, 获得丰富的文本序列表征;针对问题二, 提出了基于Star-Transformer框架的命名实体识别模型, 借助星型拓扑结构更好地捕获动态特征;同时利用高速网络优化Star-Transformer中的信息桥接, 提升模型的鲁棒性。本文提出的轻量级命名实体识别模型取得了目前Weibo语料上最好的效果。

named-entity-recognition Named Entity Recognition +1

Sparse Adversarial Attack via Perturbation Factorization

1 code implementation ECCV 2020 Yanbo Fan, Baoyuan Wu, Tuanhui Li, Yong Zhang, Mingyang Li, Zhifeng Li, Yujiu Yang

Based on this factorization, we formulate the sparse attack problem as a mixed integer programming (MIP) to jointly optimize the binary selection factors and continuous perturbation magnitudes of all pixels, with a cardinality constraint on selection factors to explicitly control the degree of sparsity.

Adversarial Attack

Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues

no code implementations14 Feb 2024 Zhiyuan Chang, Mingyang Li, Yi Liu, Junjie Wang, Qing Wang, Yang Liu

With the development of LLMs, the security threats of LLMs are getting more and more attention.

Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning

no code implementations13 Feb 2024 Mingyang Li, Hongyu Liu, Yixuan Li, Zejun Wang, Yuan Yuan, Honglin Dai

Overall, this study successfully overcomes the challenge of missing data and provides valuable insights into early detection of Alzheimer's disease, demonstrating its unique research value and practical significance.

SemanticSLAM: Learning based Semantic Map Construction and Robust Camera Localization

1 code implementation23 Jan 2024 Mingyang Li, Yue Ma, Qinru Qiu

This approach enables the creation of a semantic map of the environment and ensures reliable camera localization.

Camera Localization Pose Estimation +2

FMGS: Foundation Model Embedded 3D Gaussian Splatting for Holistic 3D Scene Understanding

no code implementations3 Jan 2024 Xingxing Zuo, Pouya Samangouei, Yunwen Zhou, Yan Di, Mingyang Li

This is achieved by distilling feature maps generated from image-based foundation models into those rendered from our 3D model.

object-detection Object Detection +1

A New Benchmark and Model for Challenging Image Manipulation Detection

no code implementations23 Nov 2023 Zhenfei Zhang, Mingyang Li, Ming-Ching Chang

Existing Image Manipulation Detection (IMD) methods are mainly based on detecting anomalous features arisen from image editing or double compression artifacts.

Image Manipulation Image Manipulation Detection

Form 10-K Itemization

no code implementations18 Feb 2023 Yanci Zhang, Mengjia Xia, Mingyang Li, Haitao Mao, Yutong Lu, Yupeng Lan, Jinlin Ye, Rui Dai

With the segmented Item sections, NLP techniques can directly apply on those Item sections related to downstream tasks.

Retrieval

Closed-Loop Transcription via Convolutional Sparse Coding

no code implementations18 Feb 2023 Xili Dai, Ke Chen, Shengbang Tong, Jingyuan Zhang, Xingjian Gao, Mingyang Li, Druv Pai, Yuexiang Zhai, Xiaojun Yuan, Heung-Yeung Shum, Lionel M. Ni, Yi Ma

Our method is arguably the first to demonstrate that a concatenation of multiple convolution sparse coding/decoding layers leads to an interpretable and effective autoencoder for modeling the distribution of large-scale natural image datasets.

Rolling Shutter Correction

Unsupervised Learning of Structured Representations via Closed-Loop Transcription

1 code implementation30 Oct 2022 Shengbang Tong, Xili Dai, Yubei Chen, Mingyang Li, Zengyi Li, Brent Yi, Yann Lecun, Yi Ma

This paper proposes an unsupervised method for learning a unified representation that serves both discriminative and generative purposes.

Revisiting Sparse Convolutional Model for Visual Recognition

1 code implementation24 Oct 2022 Xili Dai, Mingyang Li, Pengyuan Zhai, Shengbang Tong, Xingjian Gao, Shao-Lun Huang, Zhihui Zhu, Chong You, Yi Ma

We show that such models have equally strong empirical performance on CIFAR-10, CIFAR-100, and ImageNet datasets when compared to conventional neural networks.

Image Classification

SuperLine3D: Self-supervised Line Segmentation and Description for LiDAR Point Cloud

1 code implementation3 Aug 2022 Xiangrui Zhao, Sheng Yang, Tianxin Huang, Jun Chen, Teng Ma, Mingyang Li, Yong liu

To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud.

Point Cloud Registration Segmentation

DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set Feature Extraction

1 code implementation8 Mar 2022 Jiajun Fei, Ziyu Zhu, Wenlei Liu, Zhidong Deng, Mingyang Li, Huanjun Deng, Shuo Zhang

We strictly prove that any permutation-invariant function implemented by DuMLP-Pin can be decomposed into two or more permutation-equivariant ones in a dot-product way as the cardinality of the given input set is greater than a threshold.

Attribute Point Cloud Classification

Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud

no code implementations2 Mar 2022 Xiaqing Ding, Xuecheng Xu, Sha Lu, Yanmei Jiao, Mengwen Tan, Rong Xiong, Huanjun Deng, Mingyang Li, Yue Wang

Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value.

Point Cloud Registration Translation

Incremental Learning of Structured Memory via Closed-Loop Transcription

1 code implementation11 Feb 2022 Shengbang Tong, Xili Dai, Ziyang Wu, Mingyang Li, Brent Yi, Yi Ma

Our method is simpler than existing approaches for incremental learning, and more efficient in terms of model size, storage, and computation: it requires only a single, fixed-capacity autoencoding network with a feature space that is used for both discriminative and generative purposes.

Incremental Learning

Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction

1 code implementation12 Nov 2021 Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Michael Psenka, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Xiaojun Yuan, Heung Yeung Shum, Yi Ma

In particular, we propose to learn a closed-loop transcription between a multi-class multi-dimensional data distribution and a linear discriminative representation (LDR) in the feature space that consists of multiple independent multi-dimensional linear subspaces.

CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization

no code implementations21 Oct 2021 Wenzheng Hu, Zhengping Che, Ning Liu, Mingyang Li, Jian Tang, ChangShui Zhang, Jianqiang Wang

Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have explored model pruning to obtain lightweight and efficient networks.

IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach

no code implementations26 Mar 2021 Ming Zhang, Mingming Zhang, Yiming Chen, Mingyang Li

In this work, we propose a novel method for performing inertial aided navigation, by using deep neural networks (DNNs).

Sensor Fusion

Studying Politeness across Cultures Using English Twitter and Mandarin Weibo

1 code implementation6 Aug 2020 Mingyang Li, Louis Hickman, Louis Tay, Lyle Ungar, Sharath Chandra Guntuku

We study the linguistic features associated with politeness across US English and Mandarin Chinese.

Social and Information Networks Computers and Society

From A Glance to "Gotcha": Interactive Facial Image Retrieval with Progressive Relevance Feedback

no code implementations30 Jul 2020 Xinru Yang, Haozhi Qi, Mingyang Li, Alexander Hauptmann

Facial image retrieval plays a significant role in forensic investigations where an untrained witness tries to identify a suspect from a massive pool of images.

Face Image Retrieval Retrieval

Interpretable Foreground Object Search As Knowledge Distillation

no code implementations ECCV 2020 Boren Li, Po-Yu Zhuang, Jian Gu, Mingyang Li, Ping Tan

As for the proposed method, we first train a foreground encoder to learn representations of interchangeable foregrounds.

Knowledge Distillation Object +1

SEKD: Self-Evolving Keypoint Detection and Description

1 code implementation9 Jun 2020 Yafei Song, Ling Cai, Jia Li, Yonghong Tian, Mingyang Li

Researchers have attempted utilizing deep neural network (DNN) to learn novel local features from images inspired by its recent successes on a variety of vision tasks.

Homography Estimation Keypoint Detection

Semi-supervised deep learning for high-dimensional uncertainty quantification

no code implementations1 Jun 2020 Zequn Wang, Mingyang Li

Conventional uncertainty quantification methods usually lacks the capability of dealing with high-dimensional problems due to the curse of dimensionality.

Dimensionality Reduction Uncertainty Quantification +1

MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships

1 code implementation CVPR 2020 Yongjian Chen, Lei Tai, Kai Sun, Mingyang Li

Monocular 3D object detection is an essential component in autonomous driving while challenging to solve, especially for those occluded samples which are only partially visible.

Autonomous Driving Monocular 3D Object Detection +3

LE-HGR: A Lightweight and Efficient RGB-based Online Gesture Recognition Network for Embedded AR Devices

no code implementations16 Jan 2020 Hongwei Xie, Jiafang Wang, Baitao Shao, Jian Gu, Mingyang Li

Finally, we provide a variety of experimental results to show that the proposed framework is able to achieve state-of-the-art accuracy with significantly reduced computational cost, which are the key properties for enabling real-time applications in low-cost commercial devices such as mobile devices and AR/VR headsets.

3D Part Segmentation Hand Detection +2

Visual-Inertial Localization for Skid-Steering Robots with Kinematic Constraints

no code implementations13 Nov 2019 Xingxing Zuo, Mingming Zhang, Yiming Chen, Yong liu, Guoquan Huang, Mingyang Li

While visual localization or SLAM has witnessed great progress in past decades, when deploying it on a mobile robot in practice, few works have explicitly considered the kinematic (or dynamic) constraints of the real robotic system when designing state estimators.

Visual Localization

Sem-LSD: A Learning-based Semantic Line Segment Detector

1 code implementation14 Sep 2019 Yi Sun, Xushen Han, Kai Sun, Boren Li, Yongjiang Chen, Mingyang Li

Combined with high-level semantics, Sem-LS is more robust under cluttered environment compared with existing line-shaped representations.

Line Segment Detection Loop Closure Detection

Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization

no code implementations8 Sep 2019 Mingming Zhang, Xingxing Zuo, Yiming Chen, Yong liu, Mingyang Li

In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors.

6D Pose Estimation Motion Estimation

Seq-SG2SL: Inferring Semantic Layout from Scene Graph Through Sequence to Sequence Learning

no code implementations ICCV 2019 Boren Li, Boyu Zhuang, Mingyang Li, Jian Gu

The framework, called Seq-SG2SL, derives sequence proxies for the two modality and a Transformer-based seq-to-seq model learns to transduce one into the other.

Learning Local Feature Descriptor with Motion Attribute for Vision-based Localization

no code implementations3 Aug 2019 Yafei Song, Di Zhu, Jia Li, Yonghong Tian, Mingyang Li

For better performance, the features used for open-loop localization are required to be short-term globally static, and the ones used for re-localization or loop closure detection need to be long-term static.

Attribute Loop Closure Detection

2D LiDAR Map Prediction via Estimating Motion Flow with GRU

no code implementations19 Feb 2019 Yafei Song, Yonghong Tian, Gang Wang, Mingyang Li

To tackle this problem, we resort to the motion flow between adjacent maps, as motion flow is a powerful tool to process and analyze the dynamic data, which is named optical flow in video processing.

Optical Flow Estimation Representation Learning

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