---
_id: '2516'
abstract:
- lang: eng
text: 'We study the problem of object recognition for categories for which we have
no training examples, a task also called zero-data or zero-shot learning. This
situation has hardly been studied in computer vision research, even though it
occurs frequently: the world contains tens of thousands of different object classes
and for only few of them image collections have been formed and suitably annotated.
To tackle the problem we introduce attribute-based classification: objects are
identified based on a high-level description that is phrased in terms of semantic
attributes, such as the object''s color or shape. Because the identification of
each such property transcends the specific learning task at hand, the attribute
classifiers can be pre-learned independently, e.g. from existing image datasets
unrelated to the current task. Afterwards, new classes can be detected based on
their attribute representation, without the need for a new training phase. In
this paper we also introduce a new dataset, Animals with Attributes, of over 30,000
images of 50 animal classes, annotated with 85 semantic attributes. Extensive
experiments on this and two more datasets show that attribute-based classification
indeed is able to categorize images without access to any training images of the
target classes.'
author:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Hannes
full_name: Nickisch, Hannes
last_name: Nickisch
- first_name: Stefan
full_name: Harmeling, Stefan
last_name: Harmeling
citation:
ama: Lampert C, Nickisch H, Harmeling S. Attribute-based classification for zero-shot
learning of object categories. IEEE Transactions on Pattern Analysis and Machine
Intelligence. 2013;36(3):453-465. doi:10.1109/TPAMI.2013.140
apa: Lampert, C., Nickisch, H., & Harmeling, S. (2013). Attribute-based classification
for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis
and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2013.140
chicago: Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Attribute-Based
Classification for Zero-Shot Learning of Object Categories.” IEEE Transactions
on Pattern Analysis and Machine Intelligence. IEEE, 2013. https://doi.org/10.1109/TPAMI.2013.140.
ieee: C. Lampert, H. Nickisch, and S. Harmeling, “Attribute-based classification
for zero-shot learning of object categories,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 36, no. 3. IEEE, pp. 453–465, 2013.
ista: Lampert C, Nickisch H, Harmeling S. 2013. Attribute-based classification for
zero-shot learning of object categories. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 36(3), 453–465.
mla: Lampert, Christoph, et al. “Attribute-Based Classification for Zero-Shot Learning
of Object Categories.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 36, no. 3, IEEE, 2013, pp. 453–65, doi:10.1109/TPAMI.2013.140.
short: C. Lampert, H. Nickisch, S. Harmeling, IEEE Transactions on Pattern Analysis
and Machine Intelligence 36 (2013) 453–465.
date_created: 2018-12-11T11:58:08Z
date_published: 2013-07-30T00:00:00Z
date_updated: 2021-01-12T06:57:58Z
day: '30'
department:
- _id: ChLa
doi: 10.1109/TPAMI.2013.140
intvolume: ' 36'
issue: '3'
language:
- iso: eng
month: '07'
oa_version: None
page: 453 - 465
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: published
publisher: IEEE
publist_id: '4385'
quality_controlled: '1'
scopus_import: 1
status: public
title: Attribute-based classification for zero-shot learning of object categories
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2013'
...