The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation

2021/03/31 by Eu Wern Teh, Terrance DeVries, Teh, Eu Wern +9 · 2 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #cs.CV

paper · pdf · doi:10.48550/arxiv.2103.17105

To appear in the Conference on Computer and Robot Vision (CRV), 2022

arxiv created 2022/04/29 · arxiv updated 2022/05/02

Abstract

We consider the task of semi-supervised semantic segmentation, where we aim to produce pixel-wise semantic object masks given only a small number of human-labeled training examples. We focus on iterative self-training methods in which we explore the behavior of self-training over multiple refinement stages. We show that iterative self-training leads to performance degradation if done naïvely with a fixed ratio of human-labeled to pseudo-labeled training examples. We propose Greedy Iterative Self-Training (GIST) and Random Iterative Self-Training (RIST) strategies that alternate between training on either human-labeled data or pseudo-labeled data at each refinement stage, resulting in a performance boost rather than degradation. We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.

Cited by

• FRIEREN: Federated Learning with Vision-Language Regularization for Segmentation
• Rethinking Semi-supervised Segmentation Beyond Accuracy: Reliability and Robustness

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