---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Christoph Lampert
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  - foaf_Person:
      foaf_givenName: Hannes
      foaf_name: Nickisch,Hannes
      foaf_surname: Nickisch
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Harmeling,Stefan
      foaf_surname: Harmeling
  bibo_doi: 10.1109/CVPR.2009.5206594
  dct_date: 2009^xs_gYear
  dct_publisher: IEEE@
  dct_title: Learning to detect unseen object classes by between-class attribute transfer@
...