1 citations · 2 across the 5 of their papers we have counts for
5 papers
SLP-Net:An efficient lightweight network for segmentation of skin lesions
Bo Yang, Hong Peng, Chenggang Guo +3
Prompt treatment for melanoma is crucial. To assist physicians in identifying lesion areas precisely in a quick manner, we propose a novel skin lesion segmentation technique namely…
MNN: Mixed Nearest-Neighbors for Self-Supervised Learning
Xianzhong Long, Chen Peng, Yun Li
In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of posi…
Rethinking Samples Selection for Contrastive Learning: Mining of Potential Samples
Hengkui Dong, Xianzhong Long, Yun Li
Contrastive learning predicts whether two images belong to the same category by training a model to make their feature representations as close or as far away as possible. In this…
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…
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…