PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

• URL: http://arxiv.org/abs/2103.17070v1
• Date: Tue, 30 Mar 2021 00:12:10 GMT
• Title: PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering
• Authors: Jang Hyun Cho, Utkarsh Mall, Kavita Bala, Bharath Hariharan
• Abstract summary: We present a new framework for semantic segmentation without annotations via clustering. We extend clustering from images to pixels and assign separate cluster membership to different instances within each image. With our novel learning objective, our framework can learn high-level semantic concepts.
• Score: 41.39557700996688
• License: http://creativecommons.org/licenses/by/4.0/
• 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 and Cityscapes with +17.5 Acc. and +4.5 mIoU. We show that PiCIE gives a better initialization for standard supervised training. The code is available at https://github.com/janghyuncho/PiCIE.

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