4,107 research outputs found
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
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
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
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