54,712 research outputs found
Improved Training for Self-Training by Confidence Assessments
It is well known that for some tasks, labeled data sets may be hard to
gather. Therefore, we wished to tackle here the problem of having insufficient
training data. We examined learning methods from unlabeled data after an
initial training on a limited labeled data set. The suggested approach can be
used as an online learning method on the unlabeled test set. In the general
classification task, whenever we predict a label with high enough confidence,
we treat it as a true label and train the data accordingly. For the semantic
segmentation task, a classic example for an expensive data labeling process, we
do so pixel-wise. Our suggested approaches were applied on the MNIST data-set
as a proof of concept for a vision classification task and on the ADE20K
data-set in order to tackle the semi-supervised semantic segmentation problem
Explainable Semantic Medical Image Segmentation with Style
Semantic medical image segmentation using deep learning has recently achieved
high accuracy, making it appealing to clinical problems such as radiation
therapy. However, the lack of high-quality semantically labelled data remains a
challenge leading to model brittleness to small shifts to input data. Most
works require extra data for semi-supervised learning and lack the
interpretability of the boundaries of the training data distribution during
training, which is essential for model deployment in clinical practice. We
propose a fully supervised generative framework that can achieve generalisable
segmentation with only limited labelled data by simultaneously constructing an
explorable manifold during training. The proposed approach creates medical
image style paired with a segmentation task driven discriminator incorporating
end-to-end adversarial training. The discriminator is generalised to small
domain shifts as much as permissible by the training data, and the generator
automatically diversifies the training samples using a manifold of input
features learnt during segmentation. All the while, the discriminator guides
the manifold learning by supervising the semantic content and fine-grained
features separately during the image diversification. After training,
visualisation of the learnt manifold from the generator is available to
interpret the model limits. Experiments on a fully semantic, publicly available
pelvis dataset demonstrated that our method is more generalisable to shifts
than other state-of-the-art methods while being more explainable using an
explorable manifold
Semantic RGB-D Image Synthesis
Collecting diverse sets of training images for RGB-D semantic image
segmentation is not always possible. In particular, when robots need to operate
in privacy-sensitive areas like homes, the collection is often limited to a
small set of locations. As a consequence, the annotated images lack diversity
in appearance and approaches for RGB-D semantic image segmentation tend to
overfit the training data. In this paper, we thus introduce semantic RGB-D
image synthesis to address this problem. It requires synthesising a
realistic-looking RGB-D image for a given semantic label map. Current
approaches, however, are uni-modal and cannot cope with multi-modal data.
Indeed, we show that extending uni-modal approaches to multi-modal data does
not perform well. In this paper, we therefore propose a generator for
multi-modal data that separates modal-independent information of the semantic
layout from the modal-dependent information that is needed to generate an RGB
and a depth image, respectively. Furthermore, we propose a discriminator that
ensures semantic consistency between the label maps and the generated images
and perceptual similarity between the real and generated images. Our
comprehensive experiments demonstrate that the proposed method outperforms
previous uni-modal methods by a large margin and that the accuracy of an
approach for RGB-D semantic segmentation can be significantly improved by
mixing real and generated images during training
Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images
Semantic segmentation models based on convolutional neural networks (CNNs)
have gained much attention in relation to remote sensing and have achieved
remarkable performance for the extraction of buildings from high-resolution
aerial images. However, the issue of limited generalization for unseen images
remains. When there is a domain gap between the training and test datasets,
CNN-based segmentation models trained by a training dataset fail to segment
buildings for the test dataset. In this paper, we propose segmentation networks
based on a domain adaptive transfer attack (DATA) scheme for building
extraction from aerial images. The proposed system combines the domain transfer
and adversarial attack concepts. Based on the DATA scheme, the distribution of
the input images can be shifted to that of the target images while turning
images into adversarial examples against a target network. Defending
adversarial examples adapted to the target domain can overcome the performance
degradation due to the domain gap and increase the robustness of the
segmentation model. Cross-dataset experiments and the ablation study are
conducted for the three different datasets: the Inria aerial image labeling
dataset, the Massachusetts building dataset, and the WHU East Asia dataset.
Compared to the performance of the segmentation network without the DATA
scheme, the proposed method shows improvements in the overall IoU. Moreover, it
is verified that the proposed method outperforms even when compared to feature
adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure
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