1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.CV2023
Multi-network Contrastive Learning Based on Global and Local Representations
Weiquan Li, Xianzhong Long, Yun Li
The popularity of self-supervised learning has made it possible to train models without relying on labeled data, which saves expensive annotation costs. However, most existing self…
cs.CV2023
MSVQ: Self-Supervised Learning with Multiple Sample Views and Queues
Chen Peng, Xianzhong Long, Yun Li
Self-supervised methods based on contrastive learning have achieved great success in unsupervised visual representation learning. However, most methods under this framework suffer…
cs.CV2023★ 1 cited
Synthetic Hard Negative Samples for Contrastive Learning
Hengkui Dong, Xianzhong Long, Yun Li +1
Contrastive learning has emerged as an essential approach for self-supervised learning in visual representation learning. The central objective of contrastive learning is to maximi…