2,929 research outputs found
Zero-Shot Recognition using Dual Visual-Semantic Mapping Paths
Zero-shot recognition aims to accurately recognize objects of unseen classes
by using a shared visual-semantic mapping between the image feature space and
the semantic embedding space. This mapping is learned on training data of seen
classes and is expected to have transfer ability to unseen classes. In this
paper, we tackle this problem by exploiting the intrinsic relationship between
the semantic space manifold and the transfer ability of visual-semantic
mapping. We formalize their connection and cast zero-shot recognition as a
joint optimization problem. Motivated by this, we propose a novel framework for
zero-shot recognition, which contains dual visual-semantic mapping paths. Our
analysis shows this framework can not only apply prior semantic knowledge to
infer underlying semantic manifold in the image feature space, but also
generate optimized semantic embedding space, which can enhance the transfer
ability of the visual-semantic mapping to unseen classes. The proposed method
is evaluated for zero-shot recognition on four benchmark datasets, achieving
outstanding results.Comment: Accepted as a full paper in IEEE Computer Vision and Pattern
Recognition (CVPR) 201
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
Zero-Shot Hashing via Transferring Supervised Knowledge
Hashing has shown its efficiency and effectiveness in facilitating
large-scale multimedia applications. Supervised knowledge e.g. semantic labels
or pair-wise relationship) associated to data is capable of significantly
improving the quality of hash codes and hash functions. However, confronted
with the rapid growth of newly-emerging concepts and multimedia data on the
Web, existing supervised hashing approaches may easily suffer from the scarcity
and validity of supervised information due to the expensive cost of manual
labelling. In this paper, we propose a novel hashing scheme, termed
\emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories
to binary codes with hash functions learned from limited training data of
"seen" categories. Specifically, we project independent data labels i.e.
0/1-form label vectors) into semantic embedding space, where semantic
relationships among all the labels can be precisely characterized and thus seen
supervised knowledge can be transferred to unseen classes. Moreover, in order
to cope with the semantic shift problem, we rotate the embedded space to more
suitably align the embedded semantics with the low-level visual feature space,
thereby alleviating the influence of semantic gap. In the meantime, to exert
positive effects on learning high-quality hash functions, we further propose to
preserve local structural property and discrete nature in binary codes.
Besides, we develop an efficient alternating algorithm to solve the ZSH model.
Extensive experiments conducted on various real-life datasets show the superior
zero-shot image retrieval performance of ZSH as compared to several
state-of-the-art hashing methods.Comment: 11 page
Zero Shot Learning with the Isoperimetric Loss
We introduce the isoperimetric loss as a regularization criterion for
learning the map from a visual representation to a semantic embedding, to be
used to transfer knowledge to unknown classes in a zero-shot learning setting.
We use a pre-trained deep neural network model as a visual representation of
image data, a Word2Vec embedding of class labels, and linear maps between the
visual and semantic embedding spaces. However, the spaces themselves are not
linear, and we postulate the sample embedding to be populated by noisy samples
near otherwise smooth manifolds. We exploit the graph structure defined by the
sample points to regularize the estimates of the manifolds by inferring the
graph connectivity using a generalization of the isoperimetric inequalities
from Riemannian geometry to graphs. Surprisingly, this regularization alone,
paired with the simplest baseline model, outperforms the state-of-the-art among
fully automated methods in zero-shot learning benchmarks such as AwA and CUB.
This improvement is achieved solely by learning the structure of the underlying
spaces by imposing regularity.Comment: Accepted to AAAI-2
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