9,867 research outputs found
Volume-based Semantic Labeling with Signed Distance Functions
Research works on the two topics of Semantic Segmentation and SLAM
(Simultaneous Localization and Mapping) have been following separate tracks.
Here, we link them quite tightly by delineating a category label fusion
technique that allows for embedding semantic information into the dense map
created by a volume-based SLAM algorithm such as KinectFusion. Accordingly, our
approach is the first to provide a semantically labeled dense reconstruction of
the environment from a stream of RGB-D images. We validate our proposal using a
publicly available semantically annotated RGB-D dataset and a) employing ground
truth labels, b) corrupting such annotations with synthetic noise, c) deploying
a state of the art semantic segmentation algorithm based on Convolutional
Neural Networks.Comment: Submitted to PSIVT201
Proceedings of Mathsport international 2017 conference
Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017.
MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet.
Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
Stochastic partial observability poses a major challenge for decentralized
coordination in multi-agent reinforcement learning but is largely neglected in
state-of-the-art research due to a strong focus on state-based centralized
training for decentralized execution (CTDE) and benchmarks that lack sufficient
stochasticity like StarCraft Multi-Agent Challenge (SMAC). In this paper, we
propose Attention-based Embeddings of Recurrence In multi-Agent Learning
(AERIAL) to approximate value functions under stochastic partial observability.
AERIAL replaces the true state with a learned representation of multi-agent
recurrence, considering more accurate information about decentralized agent
decisions than state-based CTDE. We then introduce MessySMAC, a modified
version of SMAC with stochastic observations and higher variance in initial
states, to provide a more general and configurable benchmark regarding
stochastic partial observability. We evaluate AERIAL in Dec-Tiger as well as in
a variety of SMAC and MessySMAC maps, and compare the results with state-based
CTDE. Furthermore, we evaluate the robustness of AERIAL and state-based CTDE
against various stochasticity configurations in MessySMAC.Comment: Accepted at ICML 202
Principal Trade-off Analysis
How are the advantage relations between a set of agents playing a game
organized and how do they reflect the structure of the game? In this paper, we
illustrate "Principal Trade-off Analysis" (PTA), a decomposition method that
embeds games into a low-dimensional feature space. We argue that the embeddings
are more revealing than previously demonstrated by developing an analogy to
Principal Component Analysis (PCA). PTA represents an arbitrary two-player
zero-sum game as the weighted sum of pairs of orthogonal 2D feature planes. We
show that the feature planes represent unique strategic trade-offs and
truncation of the sequence provides insightful model reduction. We demonstrate
the validity of PTA on a quartet of games (Kuhn poker, RPS+2, Blotto, and
Pokemon). In Kuhn poker, PTA clearly identifies the trade-off between bluffing
and calling. In Blotto, PTA identifies game symmetries, and specifies strategic
trade-offs associated with distinct win conditions. These symmetries reveal
limitations of PTA unaddressed in previous work. For Pokemon, PTA recovers
clusters that naturally correspond to Pokemon types, correctly identifies the
designed trade-off between those types, and discovers a rock-paper-scissor
(RPS) cycle in the Pokemon generation type - all absent any specific
information except game outcomes.Comment: 17 pages, 8 figure
A De-biased Direct Question Approach to Measuring Consumers' Willingness to Pay
Knowledge of consumers' willingness to pay (WTP) is a prerequisite to
profitable price-setting. To gauge consumers' WTP, practitioners often rely on
a direct single question approach in which consumers are asked to explicitly
state their WTP for a product. Despite its popularity among practitioners, this
approach has been found to suffer from hypothetical bias. In this paper, we
propose a rigorous method that improves the accuracy of the direct single
question approach. Specifically, we systematically assess the hypothetical
biases associated with the direct single question approach and explore ways to
de-bias it. Our results show that by using the de-biasing procedures we
propose, we can generate a de-biased direct single question approach that is
accu-rate enough to be useful for managerial decision-making. We validate this
approach with two studies in this paper.Comment: Market Research, Pricing, Demand Estimation, Direct Estimation,
Single Question Approach, Choice Experiments, Willingness to Pay,
Hypothetical Bia
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
- …