Weakly-Supervised Learning of Visual Relations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F17%3A00318980" target="_blank" >RIV/68407700:21730/17:00318980 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/ICCV.2017.554" target="_blank" >http://dx.doi.org/10.1109/ICCV.2017.554</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCV.2017.554" target="_blank" >10.1109/ICCV.2017.554</a>

Result language

angličtina

Original language name

Weakly-Supervised Learning of Visual Relations

Original language description

This paper introduces a novel approach for modeling visual relations between pairs of objects. We call relation a triplet of the form (subject; predicate; object) where the predicate is typically a preposition (eg. 'under', 'in front of') or a verb ('hold', 'ride') that links a pair of objects (subject; object). Learning such relations is challenging as the objects have different spatial configurations and appearances depending on the relation in which they occur. Another major challenge comes from the difficulty to get annotations, especially at box-level, for all possible triplets, which makes both learning and evaluation difficult. The contributions of this paper are threefold. First, we design strong yet flexible visual features that encode the appearance and spatial configuration for pairs of objects. Second, we propose a weakly-supervised discriminative clustering model to learn relations from image-level labels only. Third we introduce a new challenging dataset of unusual relations (UnRel) together with an exhaustive annotation, that enables accurate evaluation of visual relation retrieval. We show experimentally that our model results in state-of-theart results on the visual relationship dataset [32] significantly improving performance on previously unseen relations (zero-shot learning), and confirm this observation on our newly introduced UnRel dataset.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2017 IEEE International Conference on Computer Vision (ICCV 2017)

ISBN

978-1-5386-1032-9

ISSN

1550-5499

e-ISSN

—

Number of pages

10

Pages from-to

5189-5198

Publisher name

IEEE

Place of publication

Piscataway

Event location

Venice

Event date

Oct 22, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000425498405029