Search Results for author: Haihong Tang

Found 10 papers, 0 papers with code

Collaboration by Competition: Self-coordinated Knowledge Amalgamation for Multi-talent Student Learning

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.

Shape Controllable Virtual Try-on for Underwear Models

no code implementations28 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.

Graph Attention Virtual Try-on

Path-based Deep Network for Candidate Item Matching in Recommenders

no code implementations18 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.

Recommendation Systems

A Hybrid Bandit Framework for Diversified Recommendation

no code implementations24 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.

online learning Recommendation Systems

Hearing Lips: Improving Lip Reading by Distilling Speech Recognizers

no code implementations26 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

Knowledge Distillation Lipreading +2

Recent Advances in Diversified Recommendation

no code implementations16 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.

Recommendation Systems

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