743 research outputs found
Query-guided End-to-End Person Search
Person search has recently gained attention as the novel task of finding a
person, provided as a cropped sample, from a gallery of non-cropped images,
whereby several other people are also visible. We believe that i. person
detection and re-identification should be pursued in a joint optimization
framework and that ii. the person search should leverage the query image
extensively (e.g. emphasizing unique query patterns). However, so far, no prior
art realizes this. We introduce a novel query-guided end-to-end person search
network (QEEPS) to address both aspects. We leverage a most recent joint
detector and re-identification work, OIM [37]. We extend this with i. a
query-guided Siamese squeeze-and-excitation network (QSSE-Net) that uses global
context from both the query and gallery images, ii. a query-guided region
proposal network (QRPN) to produce query-relevant proposals, and iii. a
query-guided similarity subnetwork (QSimNet), to learn a query-guided
reidentification score. QEEPS is the first end-to-end query-guided detection
and re-id network. On both the most recent CUHK-SYSU [37] and PRW [46]
datasets, we outperform the previous state-of-the-art by a large margin.Comment: Accepted as poster in CVPR 201
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Given the recent advances in depth prediction from Convolutional Neural
Networks (CNNs), this paper investigates how predicted depth maps from a deep
neural network can be deployed for accurate and dense monocular reconstruction.
We propose a method where CNN-predicted dense depth maps are naturally fused
together with depth measurements obtained from direct monocular SLAM. Our
fusion scheme privileges depth prediction in image locations where monocular
SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa.
We demonstrate the use of depth prediction for estimating the absolute scale of
the reconstruction, hence overcoming one of the major limitations of monocular
SLAM. Finally, we propose a framework to efficiently fuse semantic labels,
obtained from a single frame, with dense SLAM, yielding semantically coherent
scene reconstruction from a single view. Evaluation results on two benchmark
datasets show the robustness and accuracy of our approach.Comment: 10 pages, 6 figures, IEEE Computer Society Conference on Computer
Vision and Pattern Recognition (CVPR), Hawaii, USA, June, 2017. The first two
authors contribute equally to this pape
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement
3D Point Capsule Networks
In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement.Comment: As published in CVPR 2019 (camera ready version), with supplementary
materia
Deformation of surfaces in 2D persistent homology
In the context of 2D persistent homology a new metric has been recently introduced, the coherent matching distance. In order to study this metric, the filtering function is required to present particular “regularity” properties, based on a geometrical construction of the real plane, called extended Pareto grid. This dissertation shows a new result for modifying the extended Pareto grid associated to a filtering function defined on a smooth closed surface, with values in the real plane. In future, the technical result presented here could be used to prove the genericity of the regularity conditions assumed for the filtering function
Landscapes of data sets and functoriality of persistent homology
The aim of this article is to describe a new perspective on functoriality of
persistent homology and explain its intrinsic symmetry that is often
overlooked. A data set for us is a finite collection of functions, called
measurements, with a finite domain. Such a data set might contain internal
symmetries which are effectively captured by the action of a set of the domain
endomorphisms. Different choices of the set of endomorphisms encode different
symmetries of the data set. We describe various category structures on such
enriched data sets and prove some of their properties such as decompositions
and morphism formations. We also describe a data structure, based on coloured
directed graphs, which is convenient to encode the mentioned enrichment. We
show that persistent homology preserves only some aspects of these landscapes
of enriched data sets however not all. In other words persistent homology is
not a functor on the entire category of enriched data sets. Nevertheless we
show that persistent homology is functorial locally. We use the concept of
equivariant operators to capture some of the information missed by persistent
homology
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