TL;DR本文重新思考了对比学习方法中二元实例判别的不足之处,提出了基于相关实例(即软近邻)的对比学习方法(SNCLR),该方法有效地提高了 VIT 和 CNN 编码器中的特征表示,从图像分类、目标检测和实例分割三个标准视觉识别基准方面得到了验证。
Abstract
contrastive learning methods train visual encoders by comparing views from one instance to others. Typically, the views created from one instance are set as positive, while views from other instances are negative. This binary →