3,225 research outputs found
Zero Shot Recognition with Unreliable Attributes
In principle, zero-shot learning makes it possible to train a recognition
model simply by specifying the category's attributes. For example, with
classifiers for generic attributes like \emph{striped} and \emph{four-legged},
one can construct a classifier for the zebra category by enumerating which
properties it possesses---even without providing zebra training images. In
practice, however, the standard zero-shot paradigm suffers because attribute
predictions in novel images are hard to get right. We propose a novel random
forest approach to train zero-shot models that explicitly accounts for the
unreliability of attribute predictions. By leveraging statistics about each
attribute's error tendencies, our method obtains more robust discriminative
models for the unseen classes. We further devise extensions to handle the
few-shot scenario and unreliable attribute descriptions. On three datasets, we
demonstrate the benefit for visual category learning with zero or few training
examples, a critical domain for rare categories or categories defined on the
fly.Comment: NIPS 201
Generating Visual Representations for Zero-Shot Classification
This paper addresses the task of learning an image clas-sifier when some
categories are defined by semantic descriptions only (e.g. visual attributes)
while the others are defined by exemplar images as well. This task is often
referred to as the Zero-Shot classification task (ZSC). Most of the previous
methods rely on learning a common embedding space allowing to compare visual
features of unknown categories with semantic descriptions. This paper argues
that these approaches are limited as i) efficient discrimi-native classifiers
can't be used ii) classification tasks with seen and unseen categories
(Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently.
In contrast , this paper suggests to address ZSC and GZSC by i) learning a
conditional generator using seen classes ii) generate artificial training
examples for the categories without exemplars. ZSC is then turned into a
standard supervised learning problem. Experiments with 4 generative models and
5 datasets experimentally validate the approach, giving state-of-the-art
results on both ZSC and GZSC
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
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