# Cluster2Former: Semisupervised Clustering Transformers for Video Instance Segmentation

## TL;DR

Cluster2Former is a video instance segmentation method that uses minimal annotations to achieve strong performance, reducing the need for detailed labeling.

## Contribution

The novel use of scribble-based annotations with a similarity-based constraint loss for efficient video instance segmentation.

## Key findings

- Cluster2Former achieves competitive performance using only 0.5% of annotated pixels.
- The method is computationally efficient and cost-effective for limited annotation scenarios.

## Abstract

A novel approach for video instance segmentation is presented using semisupervised learning. Our Cluster2Former model leverages scribble-based annotations for training, significantly reducing the need for comprehensive pixel-level masks. We augment a video instance segmenter, for example, the Mask2Former architecture, with similarity-based constraint loss to handle partial annotations efficiently. We demonstrate that despite using lightweight annotations (using only 0.5% of the annotated pixels), Cluster2Former achieves competitive performance on standard benchmarks. The approach offers a cost-effective and computationally efficient solution for video instance segmentation, especially in scenarios with limited annotation resources.