**SwaV**, or **Swapping Assignments Between Views**, is a self-supervised learning approach that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, it simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, SwaV uses a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view.

**Mask Scoring R-CNN** is a Mask RCNN with MaskIoU Head, which takes the instance feature and the predicted mask together as input, and predicts the IoU between input mask and ground truth mask.

Mask Scoring R-CNN

SwAV

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

**MaskFlownet** is an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision. The learned occlusion mask can be further fed into a subsequent network cascade with dual feature pyramids.

Source	Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com