37,311 research outputs found
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
Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition
Handwritten Text Recognition (HTR) is still a challenging problem because it
must deal with two important difficulties: the variability among writing
styles, and the scarcity of labelled data. To alleviate such problems,
synthetic data generation and data augmentation are typically used to train HTR
systems. However, training with such data produces encouraging but still
inaccurate transcriptions in real words. In this paper, we propose an
unsupervised writer adaptation approach that is able to automatically adjust a
generic handwritten word recognizer, fully trained with synthetic fonts,
towards a new incoming writer. We have experimentally validated our proposal
using five different datasets, covering several challenges (i) the document
source: modern and historic samples, which may involve paper degradation
problems; (ii) different handwriting styles: single and multiple writer
collections; and (iii) language, which involves different character
combinations. Across these challenging collections, we show that our system is
able to maintain its performance, thus, it provides a practical and generic
approach to deal with new document collections without requiring any expensive
and tedious manual annotation step.Comment: Accepted to WACV 202
f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning
When labeled training data is scarce, a promising data augmentation approach
is to generate visual features of unknown classes using their attributes. To
learn the class conditional distribution of CNN features, these models rely on
pairs of image features and class attributes. Hence, they can not make use of
the abundance of unlabeled data samples. In this paper, we tackle any-shot
learning problems i.e. zero-shot and few-shot, in a unified feature generating
framework that operates in both inductive and transductive learning settings.
We develop a conditional generative model that combines the strength of VAE and
GANs and in addition, via an unconditional discriminator, learns the marginal
feature distribution of unlabeled images. We empirically show that our model
learns highly discriminative CNN features for five datasets, i.e. CUB, SUN, AWA
and ImageNet, and establish a new state-of-the-art in any-shot learning, i.e.
inductive and transductive (generalized) zero- and few-shot learning settings.
We also demonstrate that our learned features are interpretable: we visualize
them by inverting them back to the pixel space and we explain them by
generating textual arguments of why they are associated with a certain label.Comment: Accepted at CVPR 201
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