Search Results for author: Zhaoyang Liu

Found 6 papers, 2 papers with code

Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing

no code implementations25 Apr 2022 Haoyue Cheng, Zhaoyang Liu, Hang Zhou, Chen Qian, Wayne Wu, LiMin Wang

This paper focuses on the weakly-supervised audio-visual video parsing task, which aims to recognize all events belonging to each modality and localize their temporal boundaries.

Denoising

Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection

no code implementations9 Dec 2021 Jiaqi Tang, Zhaoyang Liu, Chen Qian, Wayne Wu, LiMin Wang

Generic event boundary detection is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries.

14 Boundary Detection +1

CausCF: Causal Collaborative Filtering for RecommendationEffect Estimation

no code implementations28 May 2021 Xu Xie, Zhaoyang Liu, Shiwen Wu, Fei Sun, Cihang Liu, Jiawei Chen, Jinyang Gao, Bin Cui, Bolin Ding

It is based on the idea that similar users not only have a similar taste on items, but also have similar treatment effect under recommendations.

Collaborative Filtering Recommendation Systems

PURE: An Uncertainty-aware Recommendation Framework for Maximizing Expected Posterior Utility of Platform

no code implementations1 Jan 2021 Haokun Chen, Zhaoyang Liu, Chen Xu, Ziqian Chen, Jinyang Gao, Bolin Ding

In this paper, we propose a novel recommendation framework which effectively utilizes the information of user uncertainty over different item dimensions and explicitly takes into consideration the impact of display policy on user in order to achieve maximal expected posterior utility for the platform.

Contrastive Learning for Sequential Recommendation

1 code implementation27 Oct 2020 Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Bolin Ding, Bin Cui

Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical interactions.

Contrastive Learning Data Augmentation +1

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