Papers › K-means for unsupervised instance segmentation using a self-supervised transformer

K-means for unsupervised instance segmentation using a self-supervised transformer

4 Oct 2022Pattern Recognition 2022 10archive 2025-07-28

Lim SeongTaek, Park JaeEon, Lee MinYoung, Lee HongChul

Instance segmentation is a fundamental task in computer vision that assigns every pixel to an appropriate class and localizes objects into bounding boxes. However, collecting pixel-level segmentation labels is more resource- and time-consuming than collecting classification and detection labels. Herein, we present a novel approach, iterative mask refinement using a self-supervised transformer (IMST), which performs class agnostic unsupervised instance segmentation using simple K-means clustering and a self-supervised vision transformer. IMST generates pseudo-ground-truth labels that can be used to train an off-the-shelf instance segmentation model. The pseudo labels demonstrate improved performance on multiple datasets. The instance segmentation model trained on the pseudo labels outperforms state-of-the-art unsupervised instance segmentation methods on COCO20k (+4.0 average precision (AP)) and COCO val2017(+2.6 AP) without modifications to the training loss or architecture. We demonstrate that our method can be extended to tasks such as single/multiple object discovery and supervised fine-tuning instance segmentation while outperforming previous methods.

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Tasks

Instance Segmentation Object Detection Object Discovery Segmentation Semantic Segmentation Single-object discovery Unsupervised Instance Segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Single-object discovery	COCO_20k	IMST	CorLoc	72.2	#1 of 10	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Attention DINO Dense Connections Layer Normalization Linear Layer Multi-Head Attention Residual Connection Softmax Vision Transformer k-Means Clustering

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