Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers

04/25/2022
by   Tsung-Wei Ke, et al.
0

Unsupervised semantic segmentation aims to discover groupings within and across images that capture object and view-invariance of a category without external supervision. Grouping naturally has levels of granularity, creating ambiguity in unsupervised segmentation. Existing methods avoid this ambiguity and treat it as a factor outside modeling, whereas we embrace it and desire hierarchical grouping consistency for unsupervised segmentation. We approach unsupervised segmentation as a pixel-wise feature learning problem. Our idea is that a good representation shall reveal not just a particular level of grouping, but any level of grouping in a consistent and predictable manner. We enforce spatial consistency of grouping and bootstrap feature learning with co-segmentation among multiple views of the same image, and enforce semantic consistency across the grouping hierarchy with clustering transformers between coarse- and fine-grained features. We deliver the first data-driven unsupervised hierarchical semantic segmentation method called Hierarchical Segment Grouping (HSG). Capturing visual similarity and statistical co-occurrences, HSG also outperforms existing unsupervised segmentation methods by a large margin on five major object- and scene-centric benchmarks. Our code is publicly available at https://github.com/twke18/HSG .

READ FULL TEXT

page 1

page 4

page 6

page 7

page 8

page 13

page 15

page 17

research
05/22/2023

HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

Current semantic segmentation models have achieved great success under t...
research
03/30/2021

PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clustering

We present a new framework for semantic segmentation without annotations...
research
03/23/2023

CrOC: Cross-View Online Clustering for Dense Visual Representation Learning

Learning dense visual representations without labels is an arduous task ...
research
07/22/2023

Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping

Designing deep learning algorithms for gland segmentation is crucial for...
research
02/24/2021

Unsupervised semantic discovery through visual patterns detection

We propose a new fast fully unsupervised method to discover semantic pat...
research
11/23/2021

ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives

Selection functionality is as fundamental to vector graphics as it is fo...
research
03/25/2020

Unsupervised Learning for security of Enterprise networks by micro-segmentation

Micro-segmentation is a network security technique that requires deliver...

Please sign up or login with your details

Forgot password? Click here to reset