Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Transfer Learning approach to Heatmap Regression for Action Unit
  intensity estimation   Action Units (AUs) are geometrically-based atomic facial muscle movements
known to produce appearance changes at specific facial locations. Motivated by
this observation we propose a novel AU modelling problem that consists of
jointly estimating their localisation and intensity. To this end, we propose a
simple yet efficient approach based on Heatmap Regression that merges both
problems into a single task. A Heatmap models whether an AU occurs or not at a
given spatial location. To accommodate the joint modelling of AUs intensity, we
propose variable size heatmaps, with their amplitude and size varying according
to the labelled intensity. Using Heatmap Regression, we can inherit from the
progress recently witnessed in facial landmark localisation. Building upon the
similarities between both problems, we devise a transfer learning approach
where we exploit the knowledge of a network trained on large-scale facial
landmark datasets. In particular, we explore different alternatives for
transfer learning through a) fine-tuning, b) adaptation layers, c) attention
maps, and d) reparametrisation. Our approach effectively inherits the rich
facial features produced by a strong face alignment network, with minimal extra
computational cost. We empirically validate that our system sets a new
state-of-the-art on three popular datasets, namely BP4D, DISFA, and FERA2017.

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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: supr-con