765 citations · 982 across the 25 of their papers we have counts for
25 papers
Style-Label-Free: Cross-Speaker Style Transfer by Quantized VAE and Speaker-wise Normalization in Speech Synthesis
Chunyu Qiang, Peng Yang, Hao Che +2
Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesised speech of a target speaker's timbre. Most previous approaches rely…
Back-Translation-Style Data Augmentation for Mandarin Chinese Polyphone Disambiguation
Chunyu Qiang, Peng Yang, Hao Che +3
Conversion of Chinese Grapheme-to-Phoneme (G2P) plays an important role in Mandarin Chinese Text-To-Speech (TTS) systems, where one of the biggest challenges is the task of polypho…
A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset
Jiaxin Deng, Dong Shen, Haojie Pan +7
Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video unde…
Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia
Haojie Pan, Zepeng Zhai, Yuzhou Zhang +5
Online encyclopedias, such as Wikipedia, have been well-developed and researched in the last two decades. One can find any attributes or other information of a wiki item on a wiki…
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings
Xing Wu, Chaochen Gao, Zijia Lin +3
Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constrai…
RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval
Xing Wu, Chaochen Gao, Zijia Lin +3
Video language pre-training methods have mainly adopted sparse sampling techniques to alleviate the temporal redundancy of videos. Though effective, sparse sampling still suffers i…
TokenFlow: Rethinking Fine-grained Cross-modal Alignment in Vision-Language Retrieval
Xiaohan Zou, Changqiao Wu, Lele Cheng +1
Most existing methods in vision-language retrieval match two modalities by either comparing their global feature vectors which misses sufficient information and lacks interpretabil…
ConTextual Masked Auto-Encoder for Dense Passage Retrieval
Xing Wu, Guangyuan Ma, Meng Lin +3
Dense passage retrieval aims to retrieve the relevant passages of a query from a large corpus based on dense representations (i.e., vectors) of the query and the passages. Recent s…
Diagnosing Ensemble Few-Shot Classifiers
Weikai Yang, Xi Ye, Xingxing Zhang +6
The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually diffi…
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks
Xing Wu, Chaochen Gao, Meng Lin +3
Before entering the neural network, a token is generally converted to the corresponding one-hot representation, which is a discrete distribution of the vocabulary. Smoothed represe…
Contrastive Learning of Semantic and Visual Representations for Text Tracking
Zhuang Li, Weijia Wu, Mike Zheng Shou +4
Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video. Most existing…
DistilCSE: Effective Knowledge Distillation For Contrastive Sentence Embeddings
Chaochen Gao, Xing Wu, Peng Wang +4
Large-scale contrastive learning models can learn very informative sentence embeddings, but are hard to serve online due to the huge model size. Therefore, they often play the role…
whu-nercms at trecvid2021:instance search task
Yanrui Niu, Jingyao Yang, Ankang Lu +8
We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper. This year we participate in the automatic and interacti…
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding
Xing Wu, Chaochen Gao, Liangjun Zang +3
Contrastive learning has been attracting much attention for learning unsupervised sentence embeddings. The current state-of-the-art unsupervised method is the unsupervised SimCSE (…
Smoothed Contrastive Learning for Unsupervised Sentence Embedding
Xing Wu, Chaochen Gao, Yipeng Su +3
Contrastive learning has been gradually applied to learn high-quality unsupervised sentence embedding. Among the previous un-supervised methods, the latest state-of-the-art method,…
MlTr: Multi-label Classification with Transformer
Xing Cheng, Hezheng Lin, Xiangyu Wu +5
The task of multi-label image classification is to recognize all the object labels presented in an image. Though advancing for years, small objects, similar objects and objects wit…
CAT: Cross Attention in Vision Transformer
Hezheng Lin, Xing Cheng, Xiangyu Wu +5
Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for…
HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval
Song Liu, Haoqi Fan, Shengsheng Qian +3
Video-Text Retrieval has been a hot research topic with the growth of multimedia data on the internet. Transformer for video-text learning has attracted increasing attention due to…
Converse, Focus and Guess -- Towards Multi-Document Driven Dialogue
Han Liu, Caixia Yuan, Xiaojie Wang +3
We propose a novel task, Multi-Document Driven Dialogue (MD3), in which an agent can guess the target document that the user is interested in by leading a dialogue. To benchmark pr…
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection
Shaolei Wang, Zhongyuan Wang, Wanxiang Che +1
Most existing approaches to disfluency detection heavily rely on human-annotated corpora, which is expensive to obtain in practice. There have been several proposals to alleviate t…
Query-aware Tip Generation for Vertical Search
Yang Yang, Junmei Hao, Canjia Li +7
As a concise form of user reviews, tips have unique advantages to explain the search results, assist users' decision making, and further improve user experience in vertical search…
Leveraging Historical Interaction Data for Improving Conversational Recommender System
Kun Zhou, Wayne Xin Zhao, Hui Wang +4
Recently, conversational recommender system (CRS) has become an emerging and practical research topic. Most of the existing CRS methods focus on learning effective preference repre…
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization
Kun Zhou, Hui Wang, Wayne Xin Zhao +5
Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction l…
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang +4
Knowledge graphs capture structured information and relations between a set of entities or items. As such knowledge graphs represent an attractive source of information that could…