8 citations · 22 across the 15 of their papers we have counts for
7 papers · 1 filter
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…