**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.

**BigBird** is a [Transformer](https://paperswithcode.com/method/transformer) with a sparse attention mechanism that reduces the quadratic dependency of self-attention to linear in the number of tokens. BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties of the quadratic, full attention model.  In particular, BigBird consists of three main parts:

- A set of $g$ global tokens attending on all parts of the sequence.
- All tokens attending to a set of $w$ local neighboring tokens.
- All tokens attending to a set of $r$ random tokens.

This leads to a high performing attention mechanism scaling to much longer sequence lengths (8x).

BigBird

Big Bird: Transformers for Longer Sequences

Mask Scoring R-CNN

**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.

Source	Mask Scoring R-CNN
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com