Digging Into Pseudo Label: A Low-Budget Approach for Semi-Supervised Semantic Segmentation
IEEE ACCESS(2020)
摘要
The capability to understand visual scenes with limited labeled data has been widely concerned in the field of computer vision. Although semi-supervised learning for image classification has been extensively studied in some cases, semantic segmentation with limited data has only recently gained attention. In this work, we follow the standard semi-supervised segmentation pipeline for image classification and propose a two-branch network that can encode strong and pseudo label spaces respectively, extracting reliable supervision information from pseudo-labels to assist in training network with strong labels. Our method outperforms previous semi-supervised methods with limited annotation cost. On standard benchmark PASCAL VOC 2012 for semi-supervised semantic segmentation, the proposed approach gains fresh state-of-the-art performance.
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关键词
Semantics,Image segmentation,Training,Semisupervised learning,Pipelines,Standards,Reliability,Sematic segmentation,semi-supervised learning,computer vision,deep learning
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