15 citations · 47 across the 8 of their papers we have counts for
8 papers
Maximizing User Experience with LLMOps-Driven Personalized Recommendation Systems
Chenxi Shi, Penghao Liang, Yichao Wu +2
The integration of LLMOps into personalized recommendation systems marks a significant advancement in managing LLM-driven applications. This innovation presents both opportunities…
Research on the Application of Deep Learning-based BERT Model in Sentiment Analysis
Yichao Wu, Zhengyu Jin, Chenxi Shi +2
This paper explores the application of deep learning techniques, particularly focusing on BERT models, in sentiment analysis. It begins by introducing the fundamental concept of se…
Speed Co-Augmentation for Unsupervised Audio-Visual Pre-training
Jiangliu Wang, Jianbo Jiao, Yibing Song +5
This work aims to improve unsupervised audio-visual pre-training. Inspired by the efficacy of data augmentation in visual contrastive learning, we propose a novel speed co-augmenta…
TVTSv2: Learning Out-of-the-box Spatiotemporal Visual Representations at Scale
Ziyun Zeng, Yixiao Ge, Zhan Tong +3
The ultimate goal for foundation models is realizing task-agnostic, i.e., supporting out-of-the-box usage without task-specific fine-tuning. Although breakthroughs have been made i…
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Limin Wang, Bingkun Huang, Zhiyu Zhao +5
Scale is the primary factor for building a powerful foundation model that could well generalize to a variety of downstream tasks. However, it is still challenging to train video fo…
SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
Ziteng Gao, Zhan Tong, Limin Wang +1
Human visual recognition is a sparse process, where only a few salient visual cues are attended to rather than traversing every detail uniformly. However, most current vision netwo…