Towards Unsupervised Open World Semantic Segmentation

TL;DR

This paper presents an unsupervised method for open-world semantic segmentation that detects unknown objects, clusters them based on visual similarity, and incrementally learns new classes without human annotations.

Contribution

It introduces a novel approach combining quality assessment and clustering to enable unsupervised incremental learning of new classes in semantic segmentation.

Findings

Effective detection of unknown objects in images.

Successful incremental learning of new classes without ground truth.

Improved segmentation accuracy with minimal data.

Abstract

For the semantic segmentation of images, state-of-the-art deep neural networks (DNNs) achieve high segmentation accuracy if that task is restricted to a closed set of classes. However, as of now DNNs have limited ability to operate in an open world, where they are tasked to identify pixels belonging to unknown objects and eventually to learn novel classes, incrementally. Humans have the capability to say: I don't know what that is, but I've already seen something like that. Therefore, it is desirable to perform such an incremental learning task in an unsupervised fashion. We introduce a method where unknown objects are clustered based on visual similarity. Those clusters are utilized to define new classes and serve as training data for unsupervised incremental learning. More precisely, the connected components of a predicted semantic segmentation are assessed by a segmentation quality estimate. connected components with a low estimated prediction quality are candidates for a subsequent clustering. Additionally, the component-wise quality assessment allows for obtaining predicted segmentation masks for the image regions potentially containing unknown objects. The respective pixels of such masks are pseudo-labeled and afterwards used for re-training the DNN, i.e., without the use of ground truth generated by humans. In our experiments we demonstrate that, without access to ground truth and even with few data, a DNN's class space can be extended by a novel class, achieving considerable segmentation accuracy.