31,276 research outputs found
A game-theoretic analysis of the ESP game
“Games with a Purpose” are interactive games that users play because they are fun, with the added benefit that the outcome of play is useful work. The ESP game, developed by [von Ahn and Dabbish 2004], is an example of such a game devised to label images on the web. Since labeling images is a hard problem for computer vision algorithms and can be tedious and time-consuming for humans, the ESP game provides humans with incentive to do useful work by being enjoyable to play. We present a simple game-theoretic model of the ESP game and characterize the equilibrium behavior in our model. Our equilibrium analysis supports the fact that users appear to coordinate on low effort words. We provide an alternate model of user preferences, modeling a change that could be induced through a different scoring method, and show that equilibrium behavior in this model coordinates on high effort words. We also give sufficient conditions for coordinating on high effort words to be a Bayesian-Nash equilibrium. Our results suggest the possibility of formal incentive design in achieving desirable system-wide outcomes for the purpose of human computation, complementing existing considerations of robustness against cheating and human factors.Engineering and Applied Science
Playing for Data: Ground Truth from Computer Games
Recent progress in computer vision has been driven by high-capacity models
trained on large datasets. Unfortunately, creating large datasets with
pixel-level labels has been extremely costly due to the amount of human effort
required. In this paper, we present an approach to rapidly creating
pixel-accurate semantic label maps for images extracted from modern computer
games. Although the source code and the internal operation of commercial games
are inaccessible, we show that associations between image patches can be
reconstructed from the communication between the game and the graphics
hardware. This enables rapid propagation of semantic labels within and across
images synthesized by the game, with no access to the source code or the
content. We validate the presented approach by producing dense pixel-level
semantic annotations for 25 thousand images synthesized by a photorealistic
open-world computer game. Experiments on semantic segmentation datasets show
that using the acquired data to supplement real-world images significantly
increases accuracy and that the acquired data enables reducing the amount of
hand-labeled real-world data: models trained with game data and just 1/3 of the
CamVid training set outperform models trained on the complete CamVid training
set.Comment: Accepted to the 14th European Conference on Computer Vision (ECCV
2016
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
Ancient Coin Classification Using Graph Transduction Games
Recognizing the type of an ancient coin requires theoretical expertise and
years of experience in the field of numismatics. Our goal in this work is
automatizing this time consuming and demanding task by a visual classification
framework. Specifically, we propose to model ancient coin image classification
using Graph Transduction Games (GTG). GTG casts the classification problem as a
non-cooperative game where the players (the coin images) decide their
strategies (class labels) according to the choices made by the others, which
results with a global consensus at the final labeling. Experiments are
conducted on the only publicly available dataset which is composed of 180
images of 60 types of Roman coins. We demonstrate that our approach outperforms
the literature work on the same dataset with the classification accuracy of
73.6% and 87.3% when there are one and two images per class in the training
set, respectively
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
3D LiDAR scanners are playing an increasingly important role in autonomous
driving as they can generate depth information of the environment. However,
creating large 3D LiDAR point cloud datasets with point-level labels requires a
significant amount of manual annotation. This jeopardizes the efficient
development of supervised deep learning algorithms which are often data-hungry.
We present a framework to rapidly create point clouds with accurate point-level
labels from a computer game. The framework supports data collection from both
auto-driving scenes and user-configured scenes. Point clouds from auto-driving
scenes can be used as training data for deep learning algorithms, while point
clouds from user-configured scenes can be used to systematically test the
vulnerability of a neural network, and use the falsifying examples to make the
neural network more robust through retraining. In addition, the scene images
can be captured simultaneously in order for sensor fusion tasks, with a method
proposed to do automatic calibration between the point clouds and captured
scene images. We show a significant improvement in accuracy (+9%) in point
cloud segmentation by augmenting the training dataset with the generated
synthesized data. Our experiments also show by testing and retraining the
network using point clouds from user-configured scenes, the weakness/blind
spots of the neural network can be fixed
A Framework for Exploring and Evaluating Mechanics in Human Computation Games
Human computation games (HCGs) are a crowdsourcing approach to solving
computationally-intractable tasks using games. In this paper, we describe the
need for generalizable HCG design knowledge that accommodates the needs of both
players and tasks. We propose a formal representation of the mechanics in HCGs,
providing a structural breakdown to visualize, compare, and explore the space
of HCG mechanics. We present a methodology based on small-scale design
experiments using fixed tasks while varying game elements to observe effects on
both the player experience and the human computation task completion. Finally
we discuss applications of our framework using comparisons of prior HCGs and
recent design experiments. Ultimately, we wish to enable easier exploration and
development of HCGs, helping these games provide meaningful player experiences
while solving difficult problems.Comment: 11 pages, 5 figure
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