Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Background Mixup Data Augmentation for Hand and Object-in-Contact
  Detection   Detecting the positions of human hands and objects-in-contact (hand-object
detection) in each video frame is vital for understanding human activities from
videos. For training an object detector, a method called Mixup, which overlays
two training images to mitigate data bias, has been empirically shown to be
effective for data augmentation. However, in hand-object detection, mixing two
hand-manipulation images produces unintended biases, e.g., the concentration of
hands and objects in a specific region degrades the ability of the hand-object
detector to identify object boundaries. We propose a data-augmentation method
called Background Mixup that leverages data-mixing regularization while
reducing the unintended effects in hand-object detection. Instead of mixing two
images where a hand and an object in contact appear, we mix a target training
image with background images without hands and objects-in-contact extracted
from external image sources, and use the mixed images for training the
detector. Our experiments demonstrated that the proposed method can effectively
reduce false positives and improve the performance of hand-object detection in
both supervised and semi-supervised learning settings.

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The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: stat