28,446 research outputs found
Distribution matching for transduction
Many transductive inference algorithms assume that distributions over training and test estimates should be related, e.g. by providing a large margin of separation on both sets. We use this idea to design a transduction algorithm which can be used without modification for classification, regression, and structured estimation. At its heart we exploit the fact that for a good learner the distributions over the outputs on training and test sets should match. This is a classical two-sample problem which can be solved efficiently in its most general form by using distance measures in Hilbert Space. It turns out that a number of existing heuristics can be viewed as special cases of our approach.
Structured learning of metric ensembles with application to person re-identification
Matching individuals across non-overlapping camera networks, known as person
re-identification, is a fundamentally challenging problem due to the large
visual appearance changes caused by variations of viewpoints, lighting, and
occlusion. Approaches in literature can be categoried into two streams: The
first stream is to develop reliable features against realistic conditions by
combining several visual features in a pre-defined way; the second stream is to
learn a metric from training data to ensure strong inter-class differences and
intra-class similarities. However, seeking an optimal combination of visual
features which is generic yet adaptive to different benchmarks is a unsoved
problem, and metric learning models easily get over-fitted due to the scarcity
of training data in person re-identification. In this paper, we propose two
effective structured learning based approaches which explore the adaptive
effects of visual features in recognizing persons in different benchmark data
sets. Our framework is built on the basis of multiple low-level visual features
with an optimal ensemble of their metrics. We formulate two optimization
algorithms, CMCtriplet and CMCstruct, which directly optimize evaluation
measures commonly used in person re-identification, also known as the
Cumulative Matching Characteristic (CMC) curve.Comment: 16 pages. Extended version of "Learning to Rank in Person
Re-Identification With Metric Ensembles", at
http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Paisitkriangkrai_Learning_to_Rank_2015_CVPR_paper.html.
arXiv admin note: text overlap with arXiv:1503.0154
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Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization
This paper tackles the problem of large-scale image-based localization (IBL)
where the spatial location of a query image is determined by finding out the
most similar reference images in a large database. For solving this problem, a
critical task is to learn discriminative image representation that captures
informative information relevant for localization. We propose a novel
representation learning method having higher location-discriminating power. It
provides the following contributions: 1) we represent a place (location) as a
set of exemplar images depicting the same landmarks and aim to maximize
similarities among intra-place images while minimizing similarities among
inter-place images; 2) we model a similarity measure as a probability
distribution on L_2-metric distances between intra-place and inter-place image
representations; 3) we propose a new Stochastic Attraction and Repulsion
Embedding (SARE) loss function minimizing the KL divergence between the learned
and the actual probability distributions; 4) we give theoretical comparisons
between SARE, triplet ranking and contrastive losses. It provides insights into
why SARE is better by analyzing gradients. Our SARE loss is easy to implement
and pluggable to any CNN. Experiments show that our proposed method improves
the localization performance on standard benchmarks by a large margin.
Demonstrating the broad applicability of our method, we obtained the third
place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our
code and model are available at https://github.com/Liumouliu/deepIBL.Comment: ICC
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