Semi-Supervised Semantic Image Segmentation with Self-correcting Networks
Building a large image dataset with high-quality object masks for semantic segmentation is costly and time consuming. In this paper, we introduce a principled semi-supervised framework that only uses a small set of fully supervised images (having semantic segmentation labels and box labels) and a set of images with only object bounding box labels (we call it the weak set). Our framework trains the primary segmentation model with the aid of an ancillary model that generates initial segmentation labels for the weak set and a self-correction module that improves the generated labels during training using the increasingly accurate primary model. We introduce two variants of the self-correction module using either linear or convolutional functions. Experiments on the PASCAL VOC 2012 and Cityscape datasets show that our models trained with a small fully supervised set perform similar to, or better than, models trained with a large fully supervised set while requiring ~7x less annotation effort.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Colour augmentation for improved semi-supervised semantic segmentation
Consistency regularization describes a class of approaches that have yielded state-of-the-art results for semi-supervised classification. While semi-supervised semantic segmentation proved to be more challenging, a numbe…
ClassificationSegmentationSelf-Supervised LearningSemantic Segmentation+1Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To addre…
Data AugmentationDepth EstimationDiversityMonocular Depth Estimation+3Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To addre…
Data AugmentationDepth EstimationDomain AdaptationMonocular Depth Estimation+5A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a supervised manner, requiring large amounts of …
image-classificationImage ClassificationImage SegmentationSegmentation+2NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation involves assigning pixel-wise labels to unlabeled images at training time. This is useful in a wide range of real-world applications where collecting pixel-wise labels is not feasibl…
image-classificationImage ClassificationSegmentationSelf-Driving Cars+4