Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-task, multi-label and multi-domain learning with residual
  convolutional networks for emotion recognition   Automated emotion recognition in the wild from facial images remains a
challenging problem. Although recent advances in Deep Learning have supposed a
significant breakthrough in this topic, strong changes in pose, orientation and
point of view severely harm current approaches. In addition, the acquisition of
labeled datasets is costly, and current state-of-the-art deep learning
algorithms cannot model all the aforementioned difficulties. In this paper, we
propose to apply a multi-task learning loss function to share a common feature
representation with other related tasks. Particularly we show that emotion
recognition benefits from jointly learning a model with a detector of facial
Action Units (collective muscle movements). The proposed loss function
addresses the problem of learning multiple tasks with heterogeneously labeled
data, improving previous multi-task approaches. We validate the proposal using
two datasets acquired in non controlled environments, and an application to
predict compound facial emotion expressions.

33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an