Name
Title | ||
|---|---|---|
PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering |
Abstract | ||
|---|---|---|
We present a new framework for semantic segmentation without annotations via clustering. Off-the-shelf clustering methods are limited to curated, single-label, and object-centric images yet real-world data are dominantly uncurated, multi-label, and scene-centric. We extend clustering from images to pixels and assign separate cluster membership to different instances within each image. However, solely relying on pixel-wise feature similarity fails to learn high-level semantic concepts and overfits to low-level visual cues. We propose a method to incorporate geometric consistency as an inductive bias to learn invariance and equivariance for photometric and geometric variations. With our novel learning objective, our framework can learn high-level semantic concepts. Our method, PiCIE (Pixel-level feature Clustering using Invariance and Equivariance), is the first method capable of segmenting both things and stuff categories without any hyperparameter tuning or task-specific pre-processing. Our method largely outperforms existing baselines on COCO [31] and Cityscapes [8] with +17.5 Acc. and +4.5 mIoU. We show that PiCIE gives a better initialization for standard supervised training. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CVPR46437.2021.01652 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
DocType | ISSN | Citations |
Conference | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jang Hyun Cho | 1 | 0 | 0.68 |
| Utkarsh Mall | 2 | 8 | 1.90 |
| Kavita Bala | 3 | 2046 | 138.75 |
| Bharath Hariharan | 4 | 1052 | 65.90 |