1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2024
Image-Feature Weak-to-Strong Consistency: An Enhanced Paradigm for Semi-Supervised Learning
Zhiyu Wu, Jinshi Cui
Image-level weak-to-strong consistency serves as the predominant paradigm in semi-supervised learning~(SSL) due to its simplicity and impressive performance. Nonetheless, this appr…
cs.LG2024
AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning
Zhiyu Wu, Jinshi Cui
Existing semi-supervised learning algorithms adopt pseudo-labeling and consistency regulation techniques to introduce supervision signals for unlabeled samples. To overcome the inh…
cs.CL2023★ 1 cited
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation
Xiangyu Qin, Zhiyu Wu, Jinshi Cui +5
Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pret…