【系列论文研读】Self-supervised learning

Self-supervised Spatiotemporal Feature Learning by Video Geometric Transformations

• 处理数据：video
• 方法：
  • a set of pre-designed geometric transformations (e.g. rotating 0°, 90°, 180°, and 270°) are applied to each video
  • 预测 transformations (e.g. rotations)

 

Mix-and-Match Tuning for Self-Supervised Semantic Segmentation

• 自监督模块=打乱（mix）+重组（match）
• mix：稀疏的对目标集中的patch采样、组合
• MM的目的：让同类别的更近，不同类别的更远
• 先用self-supervised proxy task初始化，再使用MM tuning

 

Boosting Self-Supervised Learning via Knowledge Transfer

• 整体框架图
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• （a）中的pretext task是Jigsaw++ task
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  • In Jigsaw++, we replace a random number of tiles in the grid (up to 2) with (occluding) tiles from another random image