52,975 research outputs found
Deep Adaptive Learning for Writer Identification based on Single Handwritten Word Images
There are two types of information in each handwritten word image: explicit
information which can be easily read or derived directly, such as lexical
content or word length, and implicit attributes such as the author's identity.
Whether features learned by a neural network for one task can be used for
another task remains an open question. In this paper, we present a deep
adaptive learning method for writer identification based on single-word images
using multi-task learning. An auxiliary task is added to the training process
to enforce the emergence of reusable features. Our proposed method transfers
the benefits of the learned features of a convolutional neural network from an
auxiliary task such as explicit content recognition to the main task of writer
identification in a single procedure. Specifically, we propose a new adaptive
convolutional layer to exploit the learned deep features. A multi-task neural
network with one or several adaptive convolutional layers is trained
end-to-end, to exploit robust generic features for a specific main task, i.e.,
writer identification. Three auxiliary tasks, corresponding to three explicit
attributes of handwritten word images (lexical content, word length and
character attributes), are evaluated. Experimental results on two benchmark
datasets show that the proposed deep adaptive learning method can improve the
performance of writer identification based on single-word images, compared to
non-adaptive and simple linear-adaptive approaches.Comment: Under view of Pattern Recognitio
Pose-Normalized Image Generation for Person Re-identification
Person Re-identification (re-id) faces two major challenges: the lack of
cross-view paired training data and learning discriminative identity-sensitive
and view-invariant features in the presence of large pose variations. In this
work, we address both problems by proposing a novel deep person image
generation model for synthesizing realistic person images conditional on the
pose. The model is based on a generative adversarial network (GAN) designed
specifically for pose normalization in re-id, thus termed pose-normalization
GAN (PN-GAN). With the synthesized images, we can learn a new type of deep
re-id feature free of the influence of pose variations. We show that this
feature is strong on its own and complementary to features learned with the
original images. Importantly, under the transfer learning setting, we show that
our model generalizes well to any new re-id dataset without the need for
collecting any training data for model fine-tuning. The model thus has the
potential to make re-id model truly scalable.Comment: 10 pages, 5 figure
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