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Published June 22, 2022 | Version v1
Conference paper Open

Novel Class Discovery in Semantic Segmentation

  • 1. NUS
  • 2. University of Trento, Italy

Description

We introduce a new setting of Novel Class Discovery in Semantic Segmentation (NCDSS), which aims at segmenting unlabeled images containing new classes given prior knowledge from a labeled set of disjoint classes. In contrast to existing approaches that look at novel class discovery in image classification, we focus on the more challenging semantic segmentation. In NCDSS, we need to distinguish the objects and background, and to handle the existence of multiple classes within an image, which increases the difficulty in using the unlabeled data. To tackle this new setting, we leverage the labeled base data and a saliency model to coarsely cluster novel classes for model training in our basic framework. Additionally, we propose the Entropy-based Uncertainty Modeling and Self-training (EUMS) framework to overcome noisy pseudo-labels, further improving the model performance on the novel classes. Our EUMS utilizes an entropy ranking technique and a dynamic reassignment to distill clean labels, thereby making
full use of the noisy data via self-supervised learning. We build the NCDSS benchmark on the PASCAL-5i dataset and
COCO-20i dataset. Extensive experiments demonstrate the feasibility of the basic framework (achieving an average mIoU of 49.81% on PASCAL-5i) and the effectiveness of EUMS framework (outperforming the basic framework by 9.28% mIoU on PASCAL-5i).

Files

Zhao_Novel_Class_Discovery_in_Semantic_Segmentation_CVPR_2022_paper.pdf

Additional details

Funding

AI4Media – A European Excellence Centre for Media, Society and Democracy 951911
European Commission