8 citations · 22 across the 15 of their papers we have counts for
15 papers
Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems
Shengyu Zhang, Ziqi Jiang, Jiangchao Yao +7
Recommendation performance usually exhibits a long-tail distribution over users -- a small portion of head users enjoy much more accurate recommendation services than the others. W…
DisCover: Disentangled Music Representation Learning for Cover Song Identification
Jiahao Xun, Shengyu Zhang, Yanting Yang +7
In the field of music information retrieval (MIR), cover song identification (CSI) is a challenging task that aims to identify cover versions of a query song from a massive collect…
ANetQA: A Large-scale Benchmark for Fine-grained Compositional Reasoning over Untrimmed Videos
Zhou Yu, Lixiang Zheng, Zhou Zhao +4
Building benchmarks to systemically analyze different capabilities of video question answering (VideoQA) models is challenging yet crucial. Existing benchmarks often use non-compos…
Denoising Multi-modal Sequential Recommenders with Contrastive Learning
Dong Yao, Shengyu Zhang, Zhou Zhao +5
There is a rapidly-growing research interest in engaging users with multi-modal data for accurate user modeling on recommender systems. Existing multimedia recommenders have achiev…
Set-Based Face Recognition Beyond Disentanglement: Burstiness Suppression With Variance Vocabulary
Jiong Wang, Zhou Zhao, Fei Wu
Set-based face recognition (SFR) aims to recognize the face sets in the unconstrained scenario, where the appearance of same identity may change dramatically with extreme variances…
CCL4Rec: Contrast over Contrastive Learning for Micro-video Recommendation
Shengyu Zhang, Bofang Li, Dong Yao +7
Micro-video recommender systems suffer from the ubiquitous noises in users' behaviors, which might render the learned user representation indiscriminating, and lead to trivial reco…
HERO: HiErarchical spatio-tempoRal reasOning with Contrastive Action Correspondence for End-to-End Video Object Grounding
Mengze Li, Tianbao Wang, Haoyu Zhang +6
Video Object Grounding (VOG) is the problem of associating spatial object regions in the video to a descriptive natural language query. This is a challenging vision-language task t…
Re4: Learning to Re-contrast, Re-attend, Re-construct for Multi-interest Recommendation
Shengyu Zhang, Lingxiao Yang, Dong Yao +5
Effectively representing users lie at the core of modern recommender systems. Since users' interests naturally exhibit multiple aspects, it is of increasing interest to develop mul…
Intelligent Request Strategy Design in Recommender System
Xufeng Qian, Yue Xu, Fuyu Lv +6
Waterfall Recommender System (RS), a popular form of RS in mobile applications, is a stream of recommended items consisting of successive pages that can be browsed by scrolling. In…
SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech
Zhenhui Ye, Zhou Zhao, Yi Ren +1
The recent progress in non-autoregressive text-to-speech (NAR-TTS) has made fast and high-quality speech synthesis possible. However, current NAR-TTS models usually use phoneme seq…
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding
Mengze Li, Tianbao Wang, Haoyu Zhang +9
Natural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query. In spite of the great advances, most existing…
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation
Dongqi Wang, Shengyu Zhang, Zhipeng Di +3
Network pruning and knowledge distillation are two widely-known model compression methods that efficiently reduce computation cost and model size. A common problem in both pruning…
Multi-trends Enhanced Dynamic Micro-video Recommendation
Yujie Lu, Yingxuan Huang, Shengyu Zhang +4
The explosively generated micro-videos on content sharing platforms call for recommender systems to permit personalized micro-video discovery with ease. Recent advances in micro-vi…
Why Do We Click: Visual Impression-aware News Recommendation
Jiahao Xun, Shengyu Zhang, Zhou Zhao +7
There is a soaring interest in the news recommendation research scenario due to the information overload. To accurately capture users' interests, we propose to model multi-modal fe…
Future-Aware Diverse Trends Framework for Recommendation
Yujie Lu, Shengyu Zhang, Yingxuan Huang +4
In recommender systems, modeling user-item behaviors is essential for user representation learning. Existing sequential recommenders consider the sequential correlations between hi…