3,439 research outputs found
Parameter-Efficient Person Re-identification in the 3D Space
People live in a 3D world. However, existing works on person
re-identification (re-id) mostly consider the semantic representation learning
in a 2D space, intrinsically limiting the understanding of people. In this
work, we address this limitation by exploring the prior knowledge of the 3D
body structure. Specifically, we project 2D images to a 3D space and introduce
a novel parameter-efficient Omni-scale Graph Network (OG-Net) to learn the
pedestrian representation directly from 3D point clouds. OG-Net effectively
exploits the local information provided by sparse 3D points and takes advantage
of the structure and appearance information in a coherent manner. With the help
of 3D geometry information, we can learn a new type of deep re-id feature free
from noisy variants, such as scale and viewpoint. To our knowledge, we are
among the first attempts to conduct person re-identification in the 3D space.
We demonstrate through extensive experiments that the proposed method (1) eases
the matching difficulty in the traditional 2D space, (2) exploits the
complementary information of 2D appearance and 3D structure, (3) achieves
competitive results with limited parameters on four large-scale person re-id
datasets, and (4) has good scalability to unseen datasets.Comment: The code is available at https://github.com/layumi/person-reid-3
CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines
Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective.
The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines.
From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
Pointwise Convolutional Neural Networks
Deep learning with 3D data such as reconstructed point clouds and CAD models
has received great research interests recently. However, the capability of
using point clouds with convolutional neural network has been so far not fully
explored. In this paper, we present a convolutional neural network for semantic
segmentation and object recognition with 3D point clouds. At the core of our
network is pointwise convolution, a new convolution operator that can be
applied at each point of a point cloud. Our fully convolutional network design,
while being surprisingly simple to implement, can yield competitive accuracy in
both semantic segmentation and object recognition task.Comment: 10 pages, 6 figures, 10 tables. Paper accepted to CVPR 201
On three-dimensional dilational elastic metamaterials
Dilational materials are stable three-dimensional isotropic auxetics with an
ultimate Poisson's ratio of -1. We design, evaluate, fabricate, and
characterize crystalline metamaterials approaching this ideal. To reveal all
modes, we calculate the phonon band structures. On this basis, using cubic
symmetry, we can unambiguously retrieve all different non-zero elements of the
rank-4 effective metamaterial elasticity tensor, from which all effective
elastic metamaterial properties follow. While the elastic properties and the
phase velocity remain anisotropic, the effective Poisson's ratio indeed becomes
isotropic and approaches -1 in the limit of small internal connections. This
finding is also supported by independent static continuum-mechanics
calculations. In static experiments on macroscopic polymer structures
fabricated by three-dimensional printing, we measure Poisson's ratios as low as
-0.8 in good agreement with theory. Microscopic samples are also presented.Comment: 8 figure
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