3,834 research outputs found
Budget-aware Semi-Supervised Semantic and Instance Segmentation
Methods that move towards less supervised scenarios are key for image
segmentation, as dense labels demand significant human intervention. Generally,
the annotation burden is mitigated by labeling datasets with weaker forms of
supervision, e.g. image-level labels or bounding boxes. Another option are
semi-supervised settings, that commonly leverage a few strong annotations and a
huge number of unlabeled/weakly-labeled data. In this paper, we revisit
semi-supervised segmentation schemes and narrow down significantly the
annotation budget (in terms of total labeling time of the training set)
compared to previous approaches. With a very simple pipeline, we demonstrate
that at low annotation budgets, semi-supervised methods outperform by a wide
margin weakly-supervised ones for both semantic and instance segmentation. Our
approach also outperforms previous semi-supervised works at a much reduced
labeling cost. We present results for the Pascal VOC benchmark and unify weakly
and semi-supervised approaches by considering the total annotation budget, thus
allowing a fairer comparison between methods.Comment: To appear in CVPR-W 2019 (DeepVision workshop
Budget-aware semi-supervised semantic and instance segmentation
Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised ap- proaches by considering the total annotation budget, thus allowing a fairer comparison between methods.Peer ReviewedPostprint (author's final draft
Coarse-to-Fine Annotation Enrichment for Semantic Segmentation Learning
Rich high-quality annotated data is critical for semantic segmentation
learning, yet acquiring dense and pixel-wise ground-truth is both labor- and
time-consuming. Coarse annotations (e.g., scribbles, coarse polygons) offer an
economical alternative, with which training phase could hardly generate
satisfactory performance unfortunately. In order to generate high-quality
annotated data with a low time cost for accurate segmentation, in this paper,
we propose a novel annotation enrichment strategy, which expands existing
coarse annotations of training data to a finer scale. Extensive experiments on
the Cityscapes and PASCAL VOC 2012 benchmarks have shown that the neural
networks trained with the enriched annotations from our framework yield a
significant improvement over that trained with the original coarse labels. It
is highly competitive to the performance obtained by using human annotated
dense annotations. The proposed method also outperforms among other
state-of-the-art weakly-supervised segmentation methods.Comment: CIKM 2018 International Conference on Information and Knowledge
Managemen
Mask-guided sample selection for Semi-Supervised Instance Segmentation
Image segmentation methods are usually trained with pixel-level annotations,
which require significant human effort to collect. The most common solution to
address this constraint is to implement weakly-supervised pipelines trained
with lower forms of supervision, such as bounding boxes or scribbles. Another
option are semi-supervised methods, which leverage a large amount of unlabeled
data and a limited number of strongly-labeled samples. In this second setup,
samples to be strongly-annotated can be selected randomly or with an active
learning mechanism that chooses the ones that will maximize the model
performance. In this work, we propose a sample selection approach to decide
which samples to annotate for semi-supervised instance segmentation. Our method
consists in first predicting pseudo-masks for the unlabeled pool of samples,
together with a score predicting the quality of the mask. This score is an
estimate of the Intersection Over Union (IoU) of the segment with the ground
truth mask. We study which samples are better to annotate given the quality
score, and show how our approach outperforms a random selection, leading to
improved performance for semi-supervised instance segmentation with low
annotation budgets.Comment: Preprint submitted to Multimedia Tools and Application
Active Learning for Semantic Segmentation with Multi-class Label Query
This paper proposes a new active learning method for semantic segmentation.
The core of our method lies in a new annotation query design. It samples
informative local image regions (e.g., superpixels), and for each of such
regions, asks an oracle for a multi-hot vector indicating all classes existing
in the region. This multi-class labeling strategy is substantially more
efficient than existing ones like segmentation, polygon, and even dominant
class labeling in terms of annotation time per click. However, it introduces
the class ambiguity issue in training since it assigns partial labels (i.e., a
set of candidate classes) to individual pixels. We thus propose a new algorithm
for learning semantic segmentation while disambiguating the partial labels in
two stages. In the first stage, it trains a segmentation model directly with
the partial labels through two new loss functions motivated by partial label
learning and multiple instance learning. In the second stage, it disambiguates
the partial labels by generating pixel-wise pseudo labels, which are used for
supervised learning of the model. Equipped with a new acquisition function
dedicated to the multi-class labeling, our method outperformed previous work on
Cityscapes and PASCAL VOC 2012 while spending less annotation cost
A comprehensive survey on deep active learning and its applications in medical image analysis
Deep learning has achieved widespread success in medical image analysis,
leading to an increasing demand for large-scale expert-annotated medical image
datasets. Yet, the high cost of annotating medical images severely hampers the
development of deep learning in this field. To reduce annotation costs, active
learning aims to select the most informative samples for annotation and train
high-performance models with as few labeled samples as possible. In this
survey, we review the core methods of active learning, including the evaluation
of informativeness and sampling strategy. For the first time, we provide a
detailed summary of the integration of active learning with other
label-efficient techniques, such as semi-supervised, self-supervised learning,
and so on. Additionally, we also highlight active learning works that are
specifically tailored to medical image analysis. In the end, we offer our
perspectives on the future trends and challenges of active learning and its
applications in medical image analysis.Comment: Paper List on Github:
https://github.com/LightersWang/Awesome-Active-Learning-for-Medical-Image-Analysi
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