6,925 research outputs found
Learning Hypergraph-regularized Attribute Predictors
We present a novel attribute learning framework named Hypergraph-based
Attribute Predictor (HAP). In HAP, a hypergraph is leveraged to depict the
attribute relations in the data. Then the attribute prediction problem is
casted as a regularized hypergraph cut problem in which HAP jointly learns a
collection of attribute projections from the feature space to a hypergraph
embedding space aligned with the attribute space. The learned projections
directly act as attribute classifiers (linear and kernelized). This formulation
leads to a very efficient approach. By considering our model as a multi-graph
cut task, our framework can flexibly incorporate other available information,
in particular class label. We apply our approach to attribute prediction,
Zero-shot and -shot learning tasks. The results on AWA, USAA and CUB
databases demonstrate the value of our methods in comparison with the
state-of-the-art approaches.Comment: This is an attribute learning paper accepted by 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
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