7,087 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
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks
How can we reuse existing knowledge, in the form of available datasets, when
solving a new and apparently unrelated target task from a set of unlabeled
data? In this work we make a first contribution to answer this question in the
context of image classification. We frame this quest as an active learning
problem and use zero-shot classifiers to guide the learning process by linking
the new task to the existing classifiers. By revisiting the dual formulation of
adaptive SVM, we reveal two basic conditions to choose greedily only the most
relevant samples to be annotated. On this basis we propose an effective active
learning algorithm which learns the best possible target classification model
with minimum human labeling effort. Extensive experiments on two challenging
datasets show the value of our approach compared to the state-of-the-art active
learning methodologies, as well as its potential to reuse past datasets with
minimal effort for future tasks
A Novel BiLevel Paradigm for Image-to-Image Translation
Image-to-image (I2I) translation is a pixel-level mapping that requires a
large number of paired training data and often suffers from the problems of
high diversity and strong category bias in image scenes. In order to tackle
these problems, we propose a novel BiLevel (BiL) learning paradigm that
alternates the learning of two models, respectively at an instance-specific
(IS) and a general-purpose (GP) level. In each scene, the IS model learns to
maintain the specific scene attributes. It is initialized by the GP model that
learns from all the scenes to obtain the generalizable translation knowledge.
This GP initialization gives the IS model an efficient starting point, thus
enabling its fast adaptation to the new scene with scarce training data. We
conduct extensive I2I translation experiments on human face and street view
datasets. Quantitative results validate that our approach can significantly
boost the performance of classical I2I translation models, such as PG2 and
Pix2Pix. Our visualization results show both higher image quality and more
appropriate instance-specific details, e.g., the translated image of a person
looks more like that person in terms of identity
Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs
Crowdsourcing platforms are now extensively used for conducting subjective
pairwise comparison studies. In this setting, a pairwise comparison dataset is
typically gathered via random sampling, either \emph{with} or \emph{without}
replacement. In this paper, we use tools from random graph theory to analyze
these two random sampling methods for the HodgeRank estimator. Using the
Fiedler value of the graph as a measurement for estimator stability
(informativeness), we provide a new estimate of the Fiedler value for these two
random graph models. In the asymptotic limit as the number of vertices tends to
infinity, we prove the validity of the estimate. Based on our findings, for a
small number of items to be compared, we recommend a two-stage sampling
strategy where a greedy sampling method is used initially and random sampling
\emph{without} replacement is used in the second stage. When a large number of
items is to be compared, we recommend random sampling with replacement as this
is computationally inexpensive and trivially parallelizable. Experiments on
synthetic and real-world datasets support our analysis
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