149 citations · 161 across the 5 of their papers we have counts for
5 papers
A Vlogger-augmented Graph Neural Network Model for Micro-video Recommendation
Weijiang Lai, Beihong Jin, Beibei Li +2
Existing micro-video recommendation models exploit the interactions between users and micro-videos and/or multi-modal information of micro-videos to predict the next micro-video a…
TeachCLIP: Multi-Grained Teaching for Efficient Text-to-Video Retrieval
Kaibin Tian, Ruixiang Zhao, Hu Hu +4
For text-to-video retrieval (T2VR), which aims to retrieve unlabeled videos by ad-hoc textual queries, CLIP-based methods are dominating. Compared to CLIP4Clip which is efficient a…
Deeply Coupled Cross-Modal Prompt Learning
Xuejing Liu, Wei Tang, Jinghui Lu +3
Recent advancements in multimodal foundation models (e.g., CLIP) have excelled in zero-shot generalization. Prompt tuning involved in the knowledge transfer from foundation models…
ChinaOpen: A Dataset for Open-world Multimodal Learning
Aozhu Chen, Ziyuan Wang, Chengbo Dong +5
This paper introduces ChinaOpen, a dataset sourced from Bilibili, a popular Chinese video-sharing website, for open-world multimodal learning. While the state-of-the-art multimodal…
Deep Learning and Its Applications to Machine Health Monitoring: A Survey
Rui Zhao, Ruqiang Yan, Zhenghua Chen +3
Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image…