no code implementations • ECCV 2020 • Sihui Luo, Wenwen Pan, Xinchao Wang, Dazhou Wang, Haihong Tang, Mingli Song
To this end, we propose a self-coordinate knowledge amalgamation network (SOKA-Net) for learning the multi-talent student model.
no code implementations • 28 Jul 2021 • Xin Gao, Zhenjiang Liu, Zunlei Feng, Chengji Shen, Kairi Ou, Haihong Tang, Mingli Song
Existing 2D image-based virtual try-on methods aim to transfer a target clothing image onto a reference person, which has two main disadvantages: cannot control the size and length precisely; unable to accurately estimate the user's figure in the case of users wearing thick clothes, resulting in inaccurate dressing effect.
no code implementations • 26 May 2021 • Yong Qian, Zhongqing Wang, Rong Xiao, Chen Chen, Haihong Tang
Previous studies show effective of pre-trained language models for sentiment analysis.
no code implementations • 18 May 2021 • Houyi Li, Zhihong Chen, Chenliang Li, Rong Xiao, Hongbo Deng, Peng Zhang, Yongchao Liu, Haihong Tang
PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.
no code implementations • 19 Apr 2021 • Chenyi Lei, Shixian Luo, Yong liu, Wanggui He, Jiamang Wang, Guoxin Wang, Haihong Tang, Chunyan Miao, Houqiang Li
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
no code implementations • 24 Dec 2020 • Qinxu Ding, Yong liu, Chunyan Miao, Fei Cheng, Haihong Tang
Previous interactive recommendation methods primarily focus on learning users' personalized preferences on the relevance properties of an item set.
no code implementations • 23 Oct 2020 • Yong liu, Susen Yang, Chenyi Lei, Guoxin Wang, Haihong Tang, Juyong Zhang, Aixin Sun, Chunyan Miao
Side information of items, e. g., images and text description, has shown to be effective in contributing to accurate recommendations.
no code implementations • 26 Nov 2019 • Ya Zhao, Rui Xu, Xinchao Wang, Peng Hou, Haihong Tang, Mingli Song
In this paper, we propose a new method, termed as Lip by Speech (LIBS), of which the goal is to strengthen lip reading by learning from speech recognizers.
Ranked #1 on
Lipreading
on CMLR
no code implementations • 16 May 2019 • Qiong Wu, Yong liu, Chunyan Miao, Yin Zhao, Lu Guan, Haihong Tang
With the rapid development of recommender systems, accuracy is no longer the only golden criterion for evaluating whether the recommendation results are satisfying or not.
no code implementations • 24 Feb 2019 • Ryuichi Takanobu, Tao Zhuang, Minlie Huang, Jun Feng, Haihong Tang, Bo Zheng
In this paper, we investigate the task of aggregating search results from heterogeneous sources in an E-commerce environment.
Hierarchical Reinforcement Learning
reinforcement-learning
+1