2,143 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
Click Carving: Segmenting Objects in Video with Point Clicks
We present a novel form of interactive video object segmentation where a few
clicks by the user helps the system produce a full spatio-temporal segmentation
of the object of interest. Whereas conventional interactive pipelines take the
user's initialization as a starting point, we show the value in the system
taking the lead even in initialization. In particular, for a given video frame,
the system precomputes a ranked list of thousands of possible segmentation
hypotheses (also referred to as object region proposals) using image and motion
cues. Then, the user looks at the top ranked proposals, and clicks on the
object boundary to carve away erroneous ones. This process iterates (typically
2-3 times), and each time the system revises the top ranked proposal set, until
the user is satisfied with a resulting segmentation mask. Finally, the mask is
propagated across the video to produce a spatio-temporal object tube. On three
challenging datasets, we provide extensive comparisons with both existing work
and simpler alternative methods. In all, the proposed Click Carving approach
strikes an excellent balance of accuracy and human effort. It outperforms all
similarly fast methods, and is competitive or better than those requiring 2 to
12 times the effort.Comment: A preliminary version of the material in this document was filed as
University of Texas technical report no. UT AI16-0
Annotating Object Instances with a Polygon-RNN
We propose an approach for semi-automatic annotation of object instances.
While most current methods treat object segmentation as a pixel-labeling
problem, we here cast it as a polygon prediction task, mimicking how most
current datasets have been annotated. In particular, our approach takes as
input an image crop and sequentially produces vertices of the polygon outlining
the object. This allows a human annotator to interfere at any time and correct
a vertex if needed, producing as accurate segmentation as desired by the
annotator. We show that our approach speeds up the annotation process by a
factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement
in IoU with original ground-truth, matching the typical agreement between human
annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We
further show generalization capabilities of our approach to unseen datasets
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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