---
_id: '3704'
abstract:
- lang: eng
  text: We study the problem of object classification when training and test classes
    are disjoint, i.e. no training examples of the target classes are available. This
    setup has hardly been studied in computer vision research, but it is the rule
    rather than the exception, because the world contains tens of thousands of different
    object classes and for only a very few of them image, collections have been formed
    and annotated with suitable class labels. In this paper, we tackle the problem
    by introducing attribute-based classification. It performs object detection based
    on a human-specified high-level description of the target objects instead of training
    images. The description consists of arbitrary semantic attributes, like shape,
    color or even geographic information. Because such properties transcend the specific
    learning task at hand, they can be pre-learned, e.g. from 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 order to evaluate
    our method and to facilitate research in this area, we have assembled a new large-scale
    dataset, ldquoAnimals with Attributesrdquo, of over 30,000 animal images that
    match the 50 classes in Osherson‘s classic table of how strongly humans associate
    85 semantic attributes with animal classes. Our experiments show that by using
    an attribute layer it is indeed possible to build a learning object detection
    system that does not require any training images of the target classes.
acknowledgement: This work was funded in part by the EC project CLASS, IST 027978,
  and the PASCAL2 network of excellence.
author:
- first_name: Christoph
  full_name: Christoph Lampert
  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. Learning to detect unseen object classes
    by between-class attribute transfer. In: IEEE; 2009:951-958. doi:<a href="https://doi.org/10.1109/CVPR.2009.5206594">10.1109/CVPR.2009.5206594</a>'
  apa: 'Lampert, C., Nickisch, H., &#38; Harmeling, S. (2009). Learning to detect
    unseen object classes by between-class attribute transfer (pp. 951–958). Presented
    at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href="https://doi.org/10.1109/CVPR.2009.5206594">https://doi.org/10.1109/CVPR.2009.5206594</a>'
  chicago: Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Learning to
    Detect Unseen Object Classes by Between-Class Attribute Transfer,” 951–58. IEEE,
    2009. <a href="https://doi.org/10.1109/CVPR.2009.5206594">https://doi.org/10.1109/CVPR.2009.5206594</a>.
  ieee: 'C. Lampert, H. Nickisch, and S. Harmeling, “Learning to detect unseen object
    classes by between-class attribute transfer,” presented at the CVPR: Computer
    Vision and Pattern Recognition, 2009, pp. 951–958.'
  ista: 'Lampert C, Nickisch H, Harmeling S. 2009. Learning to detect unseen object
    classes by between-class attribute transfer. CVPR: Computer Vision and Pattern
    Recognition, 951–958.'
  mla: Lampert, Christoph, et al. <i>Learning to Detect Unseen Object Classes by Between-Class
    Attribute Transfer</i>. IEEE, 2009, pp. 951–58, doi:<a href="https://doi.org/10.1109/CVPR.2009.5206594">10.1109/CVPR.2009.5206594</a>.
  short: C. Lampert, H. Nickisch, S. Harmeling, in:, IEEE, 2009, pp. 951–958.
conference:
  name: 'CVPR: Computer Vision and Pattern Recognition'
date_created: 2018-12-11T12:04:43Z
date_published: 2009-06-20T00:00:00Z
date_updated: 2021-01-12T07:51:36Z
day: '20'
doi: 10.1109/CVPR.2009.5206594
extern: 1
fulldoi: https://doi.org/10.1109/CVPR.2009.5206594
month: '06'
page: 951 - 958
publication_status: published
publisher: IEEE
publist_id: '2652'
quality_controlled: 0
status: public
title: Learning to detect unseen object classes by between-class attribute transfer
type: conference
year: '2009'
...