Search Results for author: Liang He

Found 101 papers, 30 papers with code

Integrating BERT and Score-based Feature Gates for Chinese Grammatical Error Diagnosis

no code implementations AACL (NLP-TEA) 2020 Yongchang Cao, Liang He, Robert Ridley, Xinyu Dai

This paper describes our proposed model for the Chinese Grammatical Error Diagnosis (CGED) task in NLPTEA2020.

Multi-Scale Distribution Deep Variational Autoencoder for Explanation Generation

no code implementations Findings (ACL) 2022 ZeFeng Cai, LinLin Wang, Gerard de Melo, Fei Sun, Liang He

Generating explanations for recommender systems is essential for improving their transparency, as users often wish to understand the reason for receiving a specified recommendation.

Explanation Generation Recommendation Systems

KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals

1 code implementation Findings (EMNLP) 2021 Jun Zhang, Yan Yang, Chencai Chen, Liang He, Zhou Yu

Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation.

Question Answering Recommendation Systems +1

P-Tailor: Customizing Personality Traits for Language Models via Mixture of Specialized LoRA Experts

no code implementations18 Jun 2024 Yuhao Dan, Jie zhou, Qin Chen, Junfeng Tian, Liang He

Personalized large language models (LLMs) have attracted great attention in many applications, such as intelligent education and emotional support.

Modeling Comparative Logical Relation with Contrastive Learning for Text Generation

no code implementations13 Jun 2024 Yuhao Dan, Junfeng Tian, Jie zhou, Ming Yan, Ji Zhang, Qin Chen, Liang He

Noting the data scarcity problem, we construct a Chinese Comparative Logical Relation Dataset (CLRD), which is a high-quality human-annotated dataset and challenging for text generation with descriptions of multiple entities and annotations on their comparative logical relations.

Contrastive Learning Data-to-Text Generation +2

Recent Advances of Foundation Language Models-based Continual Learning: A Survey

no code implementations28 May 2024 Yutao Yang, Jie zhou, Xuanwen Ding, Tianyu Huai, Shunyu Liu, Qin Chen, Liang He, Yuan Xie

Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV).

Class Incremental Learning Incremental Learning +1

Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving

1 code implementation24 May 2024 Jianbiao Mei, Yukai Ma, Xuemeng Yang, Licheng Wen, Xinyu Cai, Xin Li, Daocheng Fu, Bo Zhang, Pinlong Cai, Min Dou, Botian Shi, Liang He, Yong liu, Yu Qiao

Experiments also demonstrate that as the memory bank expands, the Heuristic Process with only 1. 8B parameters can inherit the knowledge from a GPT-4 powered Analytic Process and achieve continuous performance improvement.

Autonomous Driving Decision Making +1

DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical Correction

no code implementations20 May 2024 Hao Chen, Biaojie Zeng, Xin Lin, Liang He, Aimin Zhou

Math world problems correction(MWPC) is a novel task dedicated to rectifying reasoning errors in the process of solving mathematical problems.

Math Mathematical Reasoning

A safety realignment framework via subspace-oriented model fusion for large language models

1 code implementation15 May 2024 Xin Yi, Shunfan Zheng, LinLin Wang, Xiaoling Wang, Liang He

We validate that our safety realignment framework satisfies the safety requirements of a single fine-tuned model as well as multiple models during their fusion.

Instruction Following Math

FairMonitor: A Dual-framework for Detecting Stereotypes and Biases in Large Language Models

no code implementations6 May 2024 Yanhong Bai, Jiabao Zhao, Jinxin Shi, Zhentao Xie, Xingjiao Wu, Liang He

Detecting stereotypes and biases in Large Language Models (LLMs) is crucial for enhancing fairness and reducing adverse impacts on individuals or groups when these models are applied.

Fairness

Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check

no code implementations27 Mar 2024 Linhao Ye, Zhikai Lei, Jianghao Yin, Qin Chen, Jie zhou, Liang He

Retrieval-Augmented Generation (RAG) aims to generate more reliable and accurate responses, by augmenting large language models (LLMs) with the external vast and dynamic knowledge.

Conversational Question Answering Retrieval

MixRED: A Mix-lingual Relation Extraction Dataset

1 code implementation23 Mar 2024 Lingxing Kong, Yougang Chu, Zheng Ma, Jianbing Zhang, Liang He, Jiajun Chen

Relation extraction is a critical task in the field of natural language processing with numerous real-world applications.

Relation Relation Extraction

Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking

no code implementations12 Mar 2024 Yiyang Gu, Yougen Zhou, Qin Chen, Ningning Zhou, Jie zhou, Aimin Zhou, Liang He

Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection.

Depression Detection Language Modelling +2

DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models

1 code implementation1 Mar 2024 Kedi Chen, Qin Chen, Jie zhou, Yishen He, Liang He

Since large language models (LLMs) achieve significant success in recent years, the hallucination issue remains a challenge, numerous benchmarks are proposed to detect the hallucination.

Hallucination Hallucination Evaluation +1

Let's Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models

1 code implementation23 Feb 2024 Shunyu Liu, Jie zhou, Qunxi Zhu, Qin Chen, Qingchun Bai, Jun Xiao, Liang He

Aspect-Based Sentiment Analysis (ABSA) stands as a crucial task in predicting the sentiment polarity associated with identified aspects within text.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

Fine-Grained Detoxification via Instance-Level Prefixes for Large Language Models

no code implementations23 Feb 2024 Xin Yi, LinLin Wang, Xiaoling Wang, Liang He

In this paper, we propose fine-grained detoxification via instance-level prefixes (FGDILP) to mitigate toxic text without additional cost.

Text Generation

BDIQA: A New Dataset for Video Question Answering to Explore Cognitive Reasoning through Theory of Mind

no code implementations12 Feb 2024 Yuanyuan Mao, Xin Lin, Qin Ni, Liang He

This paper presents BDIQA, the first benchmark to explore the cognitive reasoning capabilities of VideoQA models in the context of ToM.

Question Answering Video Question Answering

Make BERT-based Chinese Spelling Check Model Enhanced by Layerwise Attention and Gaussian Mixture Model

no code implementations27 Dec 2023 Yongchang Cao, Liang He, Zhen Wu, Xinyu Dai

Meanwhile, to incorporate implicit hierarchical linguistic knowledge within the encoder, we propose a novel form of n-gram-based layerwise self-attention to generate a multilayer representation.

Part-Of-Speech Tagging POS +1

MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models

no code implementations20 Dec 2023 Yan Cai, LinLin Wang, Ye Wang, Gerard de Melo, Ya zhang, Yanfeng Wang, Liang He

The emergence of various medical large language models (LLMs) in the medical domain has highlighted the need for unified evaluation standards, as manual evaluation of LLMs proves to be time-consuming and labor-intensive.

Clinical Knowledge

MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA

1 code implementation19 Dec 2023 Lang Yu, Qin Chen, Jie zhou, Liang He

Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace with the changing knowledge in the world.

Document Classification Hallucination +2

Metacognition-Enhanced Few-Shot Prompting With Positive Reinforcement

no code implementations14 Dec 2023 Yu Ji, Wen Wu, Yi Hu, Hong Zheng, Liang He

Few-shot prompting elicits the remarkable abilities of large language models by equipping them with a few demonstration examples in the input.

Few-Shot Learning

Mathematical Language Models: A Survey

no code implementations12 Dec 2023 Wentao Liu, Hanglei Hu, Jie zhou, Yuyang Ding, Junsong Li, Jiayi Zeng, Mengliang He, Qin Chen, Bo Jiang, Aimin Zhou, Liang He

In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics.

UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images

no code implementations19 Nov 2023 Weijie Li, Yitian Wan, Xingjiao Wu, Junjie Xu, Cheng Jin, Liang He

Then, to better utilize image attributes in aesthetic assessment, we propose the Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both absolute and relative attributes of images.

Attribute

Reprogramming Self-supervised Learning-based Speech Representations for Speaker Anonymization

no code implementations17 Nov 2023 Xiaojiao Chen, Sheng Li, Jiyi Li, Hao Huang, Yang Cao, Liang He

Current speaker anonymization methods, especially with self-supervised learning (SSL) models, require massive computational resources when hiding speaker identity.

Self-Supervised Learning

GhostVec: A New Threat to Speaker Privacy of End-to-End Speech Recognition System

no code implementations17 Nov 2023 Xiaojiao Chen, Sheng Li, Jiyi Li, Hao Huang, Yang Cao, Liang He

This paper demonstrates that an attacker can extract speaker information by querying speaker-adapted speech recognition (ASR) systems.

Decoder Privacy Preserving +3

Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction

1 code implementation14 Nov 2023 Kunting Li, Yong Hu, Shaolei Wang, Hanhan Ma, Liang He, Fandong Meng, Jie zhou

However, in the Chinese Spelling Correction (CSC) task, we observe a discrepancy: while ChatGPT performs well under human evaluation, it scores poorly according to traditional metrics.

Semantic Similarity Semantic Textual Similarity +1

DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding

1 code implementation29 Oct 2023 Anran Wu, Luwei Xiao, Xingjiao Wu, Shuwen Yang, Junjie Xu, Zisong Zhuang, Nian Xie, Cheng Jin, Liang He

Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document.

Answer Generation Chart Question Answering +5

Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering

no code implementations15 Oct 2023 Shuwen Yang, Anran Wu, Xingjiao Wu, Luwei Xiao, Tianlong Ma, Cheng Jin, Liang He

Firstly, utilizing compressed evidence features as input to the model results in the loss of fine-grained information within the evidence.

Contrastive Learning Logical Sequence +2

Towards Better Chain-of-Thought Prompting Strategies: A Survey

no code implementations8 Oct 2023 Zihan Yu, Liang He, Zhen Wu, Xinyu Dai, Jiajun Chen

Chain-of-Thought (CoT), a step-wise and coherent reasoning chain, shows its impressive strength when used as a prompting strategy for large language models (LLM).

DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models

2 code implementations28 Sep 2023 Licheng Wen, Daocheng Fu, Xin Li, Xinyu Cai, Tao Ma, Pinlong Cai, Min Dou, Botian Shi, Liang He, Yu Qiao

Recent advancements in autonomous driving have relied on data-driven approaches, which are widely adopted but face challenges including dataset bias, overfitting, and uninterpretability.

Autonomous Driving Common Sense Reasoning +1

3D Multiple Object Tracking on Autonomous Driving: A Literature Review

no code implementations27 Sep 2023 Peng Zhang, Xin Li, Liang He, Xin Lin

This paper undertakes a comprehensive examination, assessment, and synthesis of the research landscape in this domain, remaining attuned to the latest developments in 3D MOT while suggesting prospective avenues for future investigation.

3D Multi-Object Tracking Autonomous Driving +1

FairMonitor: A Four-Stage Automatic Framework for Detecting Stereotypes and Biases in Large Language Models

no code implementations21 Aug 2023 Yanhong Bai, Jiabao Zhao, Jinxin Shi, Tingjiang Wei, Xingjiao Wu, Liang He

Detecting stereotypes and biases in Large Language Models (LLMs) can enhance fairness and reduce adverse impacts on individuals or groups when these LLMs are applied.

Fairness

Graph Neural Network Backend for Speaker Recognition

no code implementations17 Aug 2023 Liang He, Ruida Li, Mengqi Niu

Currently, most speaker recognition backends, such as cosine, linear discriminant analysis (LDA), or probabilistic linear discriminant analysis (PLDA), make decisions by calculating similarity or distance between enrollment and test embeddings which are already extracted from neural networks.

Graph Neural Network Speaker Recognition

EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education

1 code implementation5 Aug 2023 Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie zhou, Liang He, Xipeng Qiu

Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).

Chatbot Language Modelling +1

Is ChatGPT a Good Personality Recognizer? A Preliminary Study

no code implementations8 Jul 2023 Yu Ji, Wen Wu, Hong Zheng, Yi Hu, Xi Chen, Liang He

Concretely, we employ a variety of prompting strategies to explore ChatGPT's ability in recognizing personality from given text, especially the level-oriented prompting strategy we designed for guiding ChatGPT in analyzing given text at a specified level.

Fairness Logical Reasoning +3

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

1 code implementation21 May 2023 Xiaotian Zhang, Chunyang Li, Yi Zong, Zhengyu Ying, Liang He, Xipeng Qiu

Large Language Models(LLMs) have demonstrated remarkable performance across various natural language processing tasks; however, how to comprehensively and accurately assess their performance becomes an urgent issue to be addressed.

PEGA: Personality-Guided Preference Aggregator for Ephemeral Group Recommendation

no code implementations18 Apr 2023 Guangze Ye, Wen Wu, Liye Shi, Wenxin Hu, Xin Chen, Liang He

The role of personality in our approach is twofold: (1) To estimate individual users' importance in a group and provide explainability; (2) to alleviate the data sparsity issue that occurred in ephemeral groups.

MMFormer: Multimodal Transformer Using Multiscale Self-Attention for Remote Sensing Image Classification

no code implementations23 Mar 2023 Bo Zhang, Zuheng Ming, Wei Feng, Yaqian Liu, Liang He, Kaixing Zhao

To benefit the complementary information between heterogeneous data, we introduce a new Multimodal Transformer (MMFormer) for Remote Sensing (RS) image classification using Hyperspectral Image (HSI) accompanied by another source of data such as Light Detection and Ranging (LiDAR).

Image Classification Remote Sensing Image Classification

A Review on Machine Theory of Mind

no code implementations21 Mar 2023 Yuanyuan Mao, Shuang Liu, Pengshuai Zhao, Qin Ni, Xin Lin, Liang He

Beliefs, desires, and intentions are the early abilities of infants and the foundation of human cognitive ability, as well as for machine with ToM.

Attribute

Self-Attentive Sequential Recommendation with Cheap Causal Convolutions

no code implementations2 Nov 2022 Jiayi Chen, Wen Wu, Liye Shi, Yu Ji, Wenxin Hu, Xi Chen, Wei Zheng, Liang He

We evaluate the effectiveness of the proposed model in terms of both accurate and calibrated sequential recommendation.

Sequential Recommendation

Incorporating Pre-training Paradigm for Antibody Sequence-Structure Co-design

no code implementations26 Oct 2022 Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng, Yingce Xia, Liang He, Shufang Xie, Tao Qin, Haiguang Liu, Kun He, Tie-Yan Liu

Specifically, we first pre-train an antibody language model based on the sequence data, then propose a one-shot way for sequence and structure generation of CDR to avoid the heavy cost and error propagation from an autoregressive manner, and finally leverage the pre-trained antibody model for the antigen-specific antibody generation model with some carefully designed modules.

Language Modelling Specificity

Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection

no code implementations18 Oct 2022 Xin Li, Botian Shi, Yuenan Hou, Xingjiao Wu, Tianlong Ma, Yikang Li, Liang He

To address these problems, we construct the homogeneous structure between the point cloud and images to avoid projective information loss by transforming the camera features into the LiDAR 3D space.

3D Object Detection Autonomous Driving +1

THUEE system description for NIST 2020 SRE CTS challenge

no code implementations12 Oct 2022 Yu Zheng, Jinghan Peng, Miao Zhao, Yufeng Ma, Min Liu, Xinyue Ma, Tianyu Liang, Tianlong Kong, Liang He, Minqiang Xu

This paper presents the system description of the THUEE team for the NIST 2020 Speaker Recognition Evaluation (SRE) conversational telephone speech (CTS) challenge.

Speaker Recognition

An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning

1 code implementation COLING 2022 Shaobin Chen, Jie zhou, Yuling Sun, Liang He

To address this problem, we present an information minimization based contrastive learning (InforMin-CL) model to retain the useful information and discard the redundant information by maximizing the mutual information and minimizing the information entropy between positive instances meanwhile for unsupervised sentence representation learning.

Contrastive Learning Semantic Textual Similarity +3

Hierarchic Temporal Convolutional Network With Cross-Domain Encoder for Music Source Separation

no code implementations IEEE Signal Processing Letters 2022 Ying Hu, Yadong Chen, Wenzhong Yang, Liang He, Hao Huang

In this paper, we propose a model which combines the complexed spectrogram domain feature and time-domain feature by a cross-domain encoder (CDE) and adopts the hierarchic temporal convolutional network (HTCN) for multiple music sources separation.

Audio Source Separation Music Source Separation +2

Enhancing Event-Level Sentiment Analysis with Structured Arguments

1 code implementation31 May 2022 Qi Zhang, Jie zhou, Qin Chen, Qinchun Bai, Liang He

Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e. g., subject, object, time and location) that have potential effects on the sentiment are not well studied.

Event Extraction Sentiment Analysis

A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis

no code implementations31 May 2022 Qi Zhang, Jie zhou, Qin Chen, Qingchun Bai, Jun Xiao, Liang He

Notably, we propose a Knowledge-Enhanced Adversarial Model (\texttt{KEAM}) with both implicit distributed and explicit structural knowledge to enhance the cross-lingual transfer.

Cross-Lingual Transfer Sentiment Analysis

CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument Extraction

1 code implementation1 May 2022 Jiaju Lin, Qin Chen, Jie zhou, Jian Jin, Liang He

Implicit event argument extraction (EAE) aims to identify arguments that could scatter over the document.

Decoder Event Argument Extraction +1

DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation

no code implementations22 Apr 2022 Jiayi Chen, Wen Wu, Liye Shi, Yu Ji, Wenxin Hu, Wei Zheng, Liang He

In this work, we focus on the calibrated recommendations for sequential recommendation, which is connected to both fairness and diversity.

Fairness Sequential Recommendation

Shifting More Attention to Visual Backbone: Query-modulated Refinement Networks for End-to-End Visual Grounding

1 code implementation CVPR 2022 Jiabo Ye, Junfeng Tian, Ming Yan, Xiaoshan Yang, Xuwu Wang, Ji Zhang, Liang He, Xin Lin

Moreover, since the backbones are query-agnostic, it is difficult to completely avoid the inconsistency issue by training the visual backbone end-to-end in the visual grounding framework.

Multimodal Reasoning Visual Grounding

Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis

no code implementations25 Jan 2022 Luwei Xiao, Xingjiao Wu, Wen Wu, Jing Yang, Liang He

This paper proposes a Multi-channel Attentive Graph Convolutional Network (MAGCN), consisting of two main components: cross-modality interactive learning and sentimental feature fusion.

Multimodal Sentiment Analysis

Long-Tail Session-based Recommendation from Calibration

no code implementations5 Dec 2021 Jiayi Chen, Wen Wu, Wei Zheng, Liang He

Accurate predictions in session-based recommendations have progressed, but a few studies have focused on skewed recommendation lists caused by popularity bias.

Session-Based Recommendations

Co-evolution Transformer for Protein Contact Prediction

1 code implementation NeurIPS 2021 He Zhang, Fusong Ju, Jianwei Zhu, Liang He, Bin Shao, Nanning Zheng, Tie-Yan Liu

These methods generally derive coevolutionary features by aggregating the learned residue representations from individual sequences with equal weights, which is inconsistent with the premise that residue co-evolutions are a reflection of collective covariation patterns of numerous homologous proteins.

Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model

no code implementations29 Oct 2021 Liang He, Shizhuo Zhang, Lijun Wu, Huanhuan Xia, Fusong Ju, He Zhang, Siyuan Liu, Yingce Xia, Jianwei Zhu, Pan Deng, Bin Shao, Tao Qin, Tie-Yan Liu

The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences.

Language Modelling Multiple Sequence Alignment +1

Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer

no code implementations14 Oct 2021 Siyuan Liu, Yusong Wang, Tong Wang, Yifan Deng, Liang He, Bin Shao, Jian Yin, Nanning Zheng, Tie-Yan Liu

The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.

Drug Discovery Molecular Docking +1

Inferring Substitutable and Complementary Products with Knowledge-Aware Path Reasoning based on Dynamic Policy Network

no code implementations7 Oct 2021 Zijing Yang, Jiabo Ye, LinLin Wang, Xin Lin, Liang He

To achieve this, existing approaches take advantage of the knowledge graphs to learn more evidences for inference, whereas they often suffer from invalid reasoning for lack of elegant decision making strategies.

Decision Making Knowledge Graphs +1

TransTCN: An Attention-based TCN Framework for Sequential Modeling

no code implementations29 Sep 2021 Yuan Chai, Liang He, Yang Zhao, Xueyan Li, Zhenxin Wang

The model was evaluated across a wide range of the tasks in time series, which are commonly used to the benchmark of TCN and recurrent networks.

Language Modelling Time Series Analysis

A Survey of Human-in-the-loop for Machine Learning

no code implementations2 Aug 2021 Xingjiao Wu, Luwei Xiao, Yixuan Sun, Junhang Zhang, Tianlong Ma, Liang He

Humans can provide training data for machine learning applications and directly accomplish tasks that are hard for computers in the pipeline with the help of machine-based approaches.

BIG-bench Machine Learning

ECNUICA at SemEval-2021 Task 11: Rule based Information Extraction Pipeline

no code implementations SEMEVAL 2021 Jiaju Lin, Jing Ling, Zhiwei Wang, Jiawei Liu, Qin Chen, Liang He

The purpose of the task was to extract triples from a paper in the Nature Language Processing field for constructing an Open Research Knowledge Graph.

Open Information Extraction

Document Layout Analysis via Dynamic Residual Feature Fusion

no code implementations7 Apr 2021 Xingjiao Wu, Ziling Hu, Xiangcheng Du, Jing Yang, Liang He

The document layout analysis (DLA) aims to split the document image into different interest regions and understand the role of each region, which has wide application such as optical character recognition (OCR) systems and document retrieval.

Document Layout Analysis Optical Character Recognition +2

Data-driven criterion for the solid-liquid transition of two-dimensional self-propelled colloidal particles far from equilibrium

no code implementations24 Feb 2021 Wei-chen Guo, Bao-quan Ai, Liang He

We establish an explicit data-driven criterion for identifying the solid-liquid transition of two-dimensional self-propelled colloidal particles in the far from equilibrium parameter regime, where the transition points predicted by different conventional empirical criteria for melting and freezing diverge.

Soft Condensed Matter Statistical Mechanics

Emergent criticality and universality class of spin and charge density wave transitions of two-component lattice Bose gases in optical cavities at finite temperature

no code implementations22 Dec 2020 Liang He, Su Yi

At the temperature scale around half of the on-site interaction energy, we find a new critical regime emerges and features, in particular, a new bicritical line and two critical lines associated with the finite temperature SDW-CDW, homogeneous-SDW, and homogeneous-CDW transition, respectively.

Quantum Gases Statistical Mechanics

SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis

1 code implementation COLING 2020 Jie zhou, Junfeng Tian, Rui Wang, Yuanbin Wu, Wenming Xiao, Liang He

However, due to the variety of users{'} emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain.

Language Modelling Sentence +1

Prompt Agnostic Essay Scorer: A Domain Generalization Approach to Cross-prompt Automated Essay Scoring

1 code implementation4 Aug 2020 Robert Ridley, Liang He, Xin-yu Dai, Shu-Jian Huang, Jia-Jun Chen

Cross-prompt automated essay scoring (AES) requires the system to use non target-prompt essays to award scores to a target-prompt essay.

Automated Essay Scoring Domain Generalization +1

SEEK: Segmented Embedding of Knowledge Graphs

1 code implementation ACL 2020 Wentao Xu, Shun Zheng, Liang He, Bin Shao, Jian Yin, Tie-Yan Liu

In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, such as recommendation and question answering.

Knowledge Graph Embedding Knowledge Graphs +2

A Real-Time Deep Network for Crowd Counting

1 code implementation16 Feb 2020 Xiaowen Shi, Xin Li, Caili Wu, Shuchen Kong, Jing Yang, Liang He

Automatic analysis of highly crowded people has attracted extensive attention from computer vision research.

Crowd Counting

THUEE system description for NIST 2019 SRE CTS Challenge

no code implementations25 Dec 2019 Yi Liu, Tianyu Liang, Can Xu, Xianwei Zhang, Xianhong Chen, Wei-Qiang Zhang, Liang He, Dandan song, Ruyun Li, Yangcheng Wu, Peng Ouyang, Shouyi Yin

This paper describes the systems submitted by the department of electronic engineering, institute of microelectronics of Tsinghua university and TsingMicro Co. Ltd. (THUEE) to the NIST 2019 speaker recognition evaluation CTS challenge.

Speaker Recognition

Cascaded Detail-Preserving Networks for Super-Resolution of Document Images

no code implementations25 Nov 2019 Zhichao Fu, Yu Kong, Yingbin Zheng, Hao Ye, Wenxin Hu, Jing Yang, Liang He

The accuracy of OCR is usually affected by the quality of the input document image and different kinds of marred document images hamper the OCR results.

Image Super-Resolution Optical Character Recognition (OCR)

Scene Text Recognition with Temporal Convolutional Encoder

no code implementations4 Nov 2019 Xiangcheng Du, Tianlong Ma, Yingbin Zheng, Hao Ye, Xingjiao Wu, Liang He

In this paper, we study text recognition framework by considering the long-term temporal dependencies in the encoder stage.

Decoder Scene Text Recognition

Fast Video Crowd Counting with a Temporal Aware Network

no code implementations4 Jul 2019 Xingjiao Wu, Baohan Xu, Yingbin Zheng, Hao Ye, Jing Yang, Liang He

Crowd counting aims to count the number of instantaneous people in a crowded space, and many promising solutions have been proposed for single image crowd counting.

Crowd Counting

Edge-Aware Deep Image Deblurring

no code implementations4 Jul 2019 Zhichao Fu, Tianlong Ma, Yingbin Zheng, Hao Ye, Jing Yang, Liang He

In this paper, we resort to human visual demands of sharp edges and propose a two-phase edge-aware deep network to improve deep image deblurring.

Deblurring Edge Detection +1

Adaptive Scenario Discovery for Crowd Counting

1 code implementation6 Dec 2018 Xingjiao Wu, Yingbin Zheng, Hao Ye, Wenxin Hu, Jing Yang, Liang He

Crowd counting, i. e., estimation number of the pedestrian in crowd images, is emerging as an important research problem with the public security applications.

Crowd Counting

Multiobjective Optimization Training of PLDA for Speaker Verification

2 code implementations25 Aug 2018 Liang He, Xianhong Chen, Can Xu, Jia Liu

Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.

Multiobjective Optimization Text-Independent Speaker Verification

Precise Temporal Action Localization by Evolving Temporal Proposals

no code implementations13 Apr 2018 Haonan Qiu, Yingbin Zheng, Hao Ye, Yao Lu, Feng Wang, Liang He

The performances of existing action localization approaches remain unsatisfactory in precisely determining the beginning and the end of an action.

Temporal Action Localization

MTGAN: Speaker Verification through Multitasking Triplet Generative Adversarial Networks

no code implementations24 Mar 2018 Wenhao Ding, Liang He

In this paper, we propose an enhanced triplet method that improves the encoding process of embeddings by jointly utilizing generative adversarial mechanism and multitasking optimization.

Sound Audio and Speech Processing

Comparison of Multiple Features and Modeling Methods for Text-dependent Speaker Verification

no code implementations14 Jul 2017 Yi Liu, Liang He, Yao Tian, Zhuzi Chen, Jia Liu, Michael T. Johnson

Additionally, we also find that even though bottleneck features perform well for text-independent speaker verification, they do not outperform MFCCs on the most challenging Imposter-Correct trials on RedDots.

Speaker Identification Speaker Recognition +2

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