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.
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.
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.
no code implementations • SemEval (NAACL) 2022 • Qi Zhang, Jie zhou, Qin Chen, Qingchun Bai, Jun Xiao, Liang He
The task aims to extract the structured sentiment information (e. g., holder, target, expression and sentiment polarity) in a text.
no code implementations • 9 May 2024 • Xuanwen Ding, Jie zhou, Liang Dou, Qin Chen, Yuanbin Wu, Chengcai Chen, Liang He
Few works propose continual learning tasks for ABSA, which aim to learn the target domain's ability while maintaining the history domains' abilities.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3
no code implementations • 6 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.
no code implementations • 27 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.
no code implementations • 23 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.
no code implementations • 17 Mar 2024 • Baiyan Zhang, Qin Chen, Jie zhou, Jian Jin, Liang He
In addition, we generate the rationales to explain why these events have causal relations.
no code implementations • 12 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.
no code implementations • 12 Mar 2024 • Yanhong Bai, Jiabao Zhao, Tingjiang Wei, Qing Cai, Liang He
This paper thoroughly analyzes the interpretability of KT algorithms.
no code implementations • 1 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.
1 code implementation • 1 Mar 2024 • Kedi Chen, Jie zhou, Qin Chen, Shunyu Liu, Liang He
Information extraction (IE) aims to extract complex structured information from the text.
no code implementations • 28 Feb 2024 • Zhenxiao Cheng, Jie zhou, Wen Wu, Qin Chen, Liang He
To address this, we propose the Information Bottleneck-based Gradient (\texttt{IBG}) explanation framework for ABSA.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
1 code implementation • 23 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
no code implementations • 23 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.
no code implementations • 12 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.
no code implementations • 27 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.
no code implementations • 20 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.
1 code implementation • 19 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.
no code implementations • 14 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.
no code implementations • 12 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.
1 code implementation • 7 Dec 2023 • Xin Li, Yeqi Bai, Pinlong Cai, Licheng Wen, Daocheng Fu, Bo Zhang, Xuemeng Yang, Xinyu Cai, Tao Ma, Jianfei Guo, Xing Gao, Min Dou, Yikang Li, Botian Shi, Yong liu, Liang He, Yu Qiao
This paper explores the emerging knowledge-driven autonomous driving technologies.
1 code implementation • 29 Nov 2023 • Xuecheng Wu, Heli Sun, Junxiao Xue, Ruofan Zhai, Xiangyan Kong, Jiayu Nie, Liang He
The prevailing use of SVs to spread emotions leads to the necessity of emotion recognition in SVs.
no code implementations • 19 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.
no code implementations • 17 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.
no code implementations • 17 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.
1 code implementation • 14 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.
1 code implementation • 29 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.
no code implementations • 15 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.
no code implementations • 8 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).
2 code implementations • 8 Oct 2023 • Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Alex Lin, Fei Huang
Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs.
2 code implementations • 28 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.
no code implementations • 27 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.
no code implementations • 21 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.
no code implementations • 17 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.
1 code implementation • 5 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 ).
no code implementations • 8 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.
1 code implementation • ICCV 2023 • Tao Ma, Xuemeng Yang, Hongbin Zhou, Xin Li, Botian Shi, Junjie Liu, Yuchen Yang, Zhizheng Liu, Liang He, Yu Qiao, Yikang Li, Hongsheng Li
Extensive experiments on Waymo Open Dataset show our DetZero outperforms all state-of-the-art onboard and offboard 3D detection methods.
1 code implementation • 21 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.
no code implementations • 18 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.
no code implementations • 23 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).
no code implementations • 21 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.
1 code implementation • CVPR 2023 • Xin Li, Tao Ma, Yuenan Hou, Botian Shi, Yuchen Yang, Youquan Liu, Xingjiao Wu, Qin Chen, Yikang Li, Yu Qiao, Liang He
Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81. 02 mAPH (L2) detection performance.
no code implementations • 21 Feb 2023 • Zhenxiao Cheng, Jie zhou, Wen Wu, Qin Chen, Liang He
Gradient-based explanation methods play an important role in the field of interpreting complex deep neural networks for NLP models.
no code implementations • 2 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.
no code implementations • 2 Nov 2022 • Kong Aik Lee, Tomi Kinnunen, Daniele Colibro, Claudio Vair, Andreas Nautsch, Hanwu Sun, Liang He, Tianyu Liang, Qiongqiong Wang, Mickael Rouvier, Pierre-Michel Bousquet, Rohan Kumar Das, Ignacio Viñals Bailo, Meng Liu, Héctor Deldago, Xuechen Liu, Md Sahidullah, Sandro Cumani, Boning Zhang, Koji Okabe, Hitoshi Yamamoto, Ruijie Tao, Haizhou Li, Alfonso Ortega Giménez, Longbiao Wang, Luis Buera
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge.
no code implementations • 26 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.
no code implementations • 18 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.
no code implementations • 12 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.
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.
no code implementations • COLING 2022 • Jie zhou, Qi Zhang, Qin Chen, Liang He, Xuanjing Huang
Event argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing.
no code implementations • 3 Jul 2022 • Ying Hu, Yuwu Tang, Hao Huang, Liang He
Speech emotion recognition (SER) is an essential part of human-computer interaction.
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.
Ranked #8 on Music Source Separation on MUSDB18
1 code implementation • 31 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.
no code implementations • 31 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.
1 code implementation • 1 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.
no code implementations • 22 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.
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.
no code implementations • 25 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.
no code implementations • 24 Jan 2022 • Xingjiao Wu, Luwei Xiao, Xiangcheng Du, Yingbin Zheng, Xin Li, Tianlong Ma, Liang He
Our framework is an unsupervised document layout analysis framework.
no code implementations • 5 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.
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.
no code implementations • 29 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.
no code implementations • 14 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.
no code implementations • 7 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.
no code implementations • Information Sciences 2021 • Xingjiao Wu, Yingbin Zheng, Tianlong Ma, Hao Ye, Liang He
Layout analysis from a document image plays an important role in document content understanding and information extraction systems.
no code implementations • 29 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.
no code implementations • 4 Aug 2021 • Xingjiao Wu, Tianlong Ma, Xin Li, Qin Chen, Liang He
The HITL select key samples by using confidence.
no code implementations • 2 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.
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.
no code implementations • SEMEVAL 2021 • Pingsheng Liu, LinLin Wang, Qian Zhao, Hao Chen, Yuxi Feng, Xin Lin, Liang He
This paper describes our system for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning.
no code implementations • 7 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.
no code implementations • EACL 2021 • Jie zhou, Yuanbin Wu, Changzhi Sun, Liang He
Modelling a word{'}s polarity in different contexts is a key task in sentiment analysis.
no code implementations • 24 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
no code implementations • 22 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
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.
1 code implementation • 4 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.
1 code implementation • SEMEVAL 2020 • Qian Zhao, Siyu Tao, Jie zhou, LinLin Wang, Xin Lin, Liang He
As a result, this model performs quite well in both validation and explanation.
no code implementations • ACL 2020 • Jiaying Hu, Yan Yang, Chencai Chen, Liang He, Zhou Yu
Dialogue state tracker is responsible for inferring user intentions through dialogue history.
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.
Ranked #1 on Link Prediction on YAGO37
1 code implementation • 16 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.
no code implementations • 25 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.
no code implementations • 25 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.
no code implementations • 4 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.
no code implementations • 4 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.
no code implementations • 4 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.
no code implementations • NAACL 2019 • Kaijia Yang, Liang He, Xin-yu Dai, Shu-Jian Huang, Jia-Jun Chen
Distant supervision has obtained great progress on relation classification task.
1 code implementation • 6 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.
2 code implementations • 25 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
no code implementations • SEMEVAL 2018 • Shiyun Chen, Maoquan Wang, Liang He
This paper presents our single model to Subtask 1 of SemEval 2018 Task 2: Emoji Prediction in English.
no code implementations • 13 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.
no code implementations • 24 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
no code implementations • 14 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.