Pruning-Guided Curriculum Learning for Semi-Supervised Semantic Segmentation

Heejo Kong, Gun Hee Lee, Suneung Kim, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This study focuses on improving the quality of pseudolabeling in the context of semi-supervised semantic segmentation. Previous studies have adopted confidence thresholding to reduce erroneous predictions in pseudo-labeled data and to enhance their qualities. However, numerous pseudolabels with high confidence scores exist in the early training stages even though their predictions are incorrect, and this ambiguity limits confidence thresholding substantially. In this paper, we present a novel method to resolve the ambiguity of confidence scores with the guidance of network pruning. A recent finding showed that network pruning severely impairs the network generalization ability on samples that are not yet well learned or represented. Inspired by this finding, we refine the confidence scores by reflecting the extent to which the predictions are affected by pruning. Furthermore, we adopted a curriculum learning strategy for the confidence score, which enables the network to learn gradually from easy to hard samples. This approach resolves the ambiguity by suppressing the learning of noisy pseudolabels, the confidence scores of which are difficult to trust owing to insufficient training in the early stages. Extensive experiments on various benchmarks demonstrate the superiority of our framework over state-of-the-art alternatives.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5903-5912
Number of pages10
ISBN (Electronic)9781665493468
DOIs
Publication statusPublished - 2023
Event23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023 - Waikoloa, United States
Duration: 2023 Jan 32023 Jan 7

Publication series

NameProceedings - 2023 IEEE Winter Conference on Applications of Computer Vision, WACV 2023

Conference

Conference23rd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2023
Country/TerritoryUnited States
CityWaikoloa
Period23/1/323/1/7

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Algorithms: Image recognition and understanding (object detection, categorization, segmentation)
  • Machine learning architectures
  • and algorithms (including transfer, low-shot, semi-, self-, and un-supervised learning)
  • formulations

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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