Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attention Transfer from Web Images for Video Recognition   Training deep learning based video classifiers for action recognition
requires a large amount of labeled videos. The labeling process is
labor-intensive and time-consuming. On the other hand, large amount of
weakly-labeled images are uploaded to the Internet by users everyday. To
harness the rich and highly diverse set of Web images, a scalable approach is
to crawl these images to train deep learning based classifier, such as
Convolutional Neural Networks (CNN). However, due to the domain shift problem,
the performance of Web images trained deep classifiers tend to degrade when
directly deployed to videos. One way to address this problem is to fine-tune
the trained models on videos, but sufficient amount of annotated videos are
still required. In this work, we propose a novel approach to transfer knowledge
from image domain to video domain. The proposed method can adapt to the target
domain (i.e. video data) with limited amount of training data. Our method maps
the video frames into a low-dimensional feature space using the
class-discriminative spatial attention map for CNNs. We design a novel Siamese
EnergyNet structure to learn energy functions on the attention maps by jointly
optimizing two loss functions, such that the attention map corresponding to a
ground truth concept would have higher energy. We conduct extensive experiments
on two challenging video recognition datasets (i.e. TVHI and UCF101), and
demonstrate the efficacy of our proposed method.

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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math
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The categories of this paper are: physics
7
The categories of this paper are: hep-th
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The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas