167 research outputs found
Characterization of Group-Strategyproof Mechanisms for Facility Location in Strictly Convex Space
We characterize the class of group-strategyproof mechanisms for the single
facility location game in any unconstrained strictly convex space. A mechanism
is \emph{group-strategyproof}, if no group of agents can misreport so that all
its members are \emph{strictly} better off. A strictly convex space is a normed
vector space where holds for any pair of different unit vectors , e.g., any space with .
We show that any deterministic, unanimous, group-strategyproof mechanism must
be dictatorial, and that any randomized, unanimous, translation-invariant,
group-strategyproof mechanism must be \emph{2-dictatorial}. Here a randomized
mechanism is 2-dictatorial if the lottery output of the mechanism must be
distributed on the line segment between two dictators' inputs. A mechanism is
translation-invariant if the output of the mechanism follows the same
translation of the input.
Our characterization directly implies that any (randomized)
translation-invariant approximation algorithm satisfying the
group-strategyproofness property has a lower bound of -approximation for
maximum cost (whenever ), and for social cost. We also find
an algorithm that -approximates the maximum cost and -approximates the
social cost, proving the bounds to be (almost) tight.Comment: Accepted to ACM Conference on Economics and Computation (EC) 202
Recursive Cascaded Networks for Unsupervised Medical Image Registration
We present recursive cascaded networks, a general architecture that enables
learning deep cascades, for deformable image registration. The proposed
architecture is simple in design and can be built on any base network. The
moving image is warped successively by each cascade and finally aligned to the
fixed image; this procedure is recursive in a way that every cascade learns to
perform a progressive deformation for the current warped image. The entire
system is end-to-end and jointly trained in an unsupervised manner. In
addition, enabled by the recursive architecture, one cascade can be iteratively
applied for multiple times during testing, which approaches a better fit
between each of the image pairs. We evaluate our method on 3D medical images,
where deformable registration is most commonly applied. We demonstrate that
recursive cascaded networks achieve consistent, significant gains and
outperform state-of-the-art methods. The performance reveals an increasing
trend as long as more cascades are trained, while the limit is not observed.
Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.Comment: Accepted to ICCV 201
MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask
Feature warping is a core technique in optical flow estimation; however, the
ambiguity caused by occluded areas during warping is a major problem that
remains unsolved. In this paper, we propose an asymmetric occlusion-aware
feature matching module, which can learn a rough occlusion mask that filters
useless (occluded) areas immediately after feature warping without any explicit
supervision. The proposed module can be easily integrated into end-to-end
network architectures and enjoys performance gains while introducing negligible
computational cost. The learned occlusion mask can be further fed into a
subsequent network cascade with dual feature pyramids with which we achieve
state-of-the-art performance. At the time of submission, our method, called
MaskFlownet, surpasses all published optical flow methods on the MPI Sintel,
KITTI 2012 and 2015 benchmarks. Code is available at
https://github.com/microsoft/MaskFlownet.Comment: CVPR 2020 (Oral
Comprehensive Information Integration Modeling Framework for Video Titling
In e-commerce, consumer-generated videos, which in general deliver consumers'
individual preferences for the different aspects of certain products, are
massive in volume. To recommend these videos to potential consumers more
effectively, diverse and catchy video titles are critical. However,
consumer-generated videos seldom accompany appropriate titles. To bridge this
gap, we integrate comprehensive sources of information, including the content
of consumer-generated videos, the narrative comment sentences supplied by
consumers, and the product attributes, in an end-to-end modeling framework.
Although automatic video titling is very useful and demanding, it is much less
addressed than video captioning. The latter focuses on generating sentences
that describe videos as a whole while our task requires the product-aware
multi-grained video analysis. To tackle this issue, the proposed method
consists of two processes, i.e., granular-level interaction modeling and
abstraction-level story-line summarization. Specifically, the granular-level
interaction modeling first utilizes temporal-spatial landmark cues, descriptive
words, and abstractive attributes to builds three individual graphs and
recognizes the intra-actions in each graph through Graph Neural Networks (GNN).
Then the global-local aggregation module is proposed to model inter-actions
across graphs and aggregate heterogeneous graphs into a holistic graph
representation. The abstraction-level story-line summarization further
considers both frame-level video features and the holistic graph to utilize the
interactions between products and backgrounds, and generate the story-line
topic of the video. We collect a large-scale dataset accordingly from
real-world data in Taobao, a world-leading e-commerce platform, and will make
the desensitized version publicly available to nourish further development of
the research community...Comment: 11 pages, 6 figures, to appear in KDD 2020 proceeding
A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision
Deep learning has the potential to revolutionize sports performance, with
applications ranging from perception and comprehension to decision. This paper
presents a comprehensive survey of deep learning in sports performance,
focusing on three main aspects: algorithms, datasets and virtual environments,
and challenges. Firstly, we discuss the hierarchical structure of deep learning
algorithms in sports performance which includes perception, comprehension and
decision while comparing their strengths and weaknesses. Secondly, we list
widely used existing datasets in sports and highlight their characteristics and
limitations. Finally, we summarize current challenges and point out future
trends of deep learning in sports. Our survey provides valuable reference
material for researchers interested in deep learning in sports applications
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