8,012 research outputs found
Review of Extreme Multilabel Classification
Extreme multilabel classification or XML, is an active area of interest in
machine learning. Compared to traditional multilabel classification, here the
number of labels is extremely large, hence, the name extreme multilabel
classification. Using classical one versus all classification wont scale in
this case due to large number of labels, same is true for any other
classifiers. Embedding of labels as well as features into smaller label space
is an essential first step. Moreover, other issues include existence of head
and tail labels, where tail labels are labels which exist in relatively smaller
number of given samples. The existence of tail labels creates issues during
embedding. This area has invited application of wide range of approaches
ranging from bit compression motivated from compressed sensing, tree based
embeddings, deep learning based latent space embedding including using
attention weights, linear algebra based embeddings such as SVD, clustering,
hashing, to name a few. The community has come up with a useful set of metrics
to identify correctly the prediction for head or tail labels.Comment: 46 pages, 13 figure
Spontaneous Subtle Expression Detection and Recognition based on Facial Strain
Optical strain is an extension of optical flow that is capable of quantifying
subtle changes on faces and representing the minute facial motion intensities
at the pixel level. This is computationally essential for the relatively new
field of spontaneous micro-expression, where subtle expressions can be
technically challenging to pinpoint. In this paper, we present a novel method
for detecting and recognizing micro-expressions by utilizing facial optical
strain magnitudes to construct optical strain features and optical strain
weighted features. The two sets of features are then concatenated to form the
resultant feature histogram. Experiments were performed on the CASME II and
SMIC databases. We demonstrate on both databases, the usefulness of optical
strain information and more importantly, that our best approaches are able to
outperform the original baseline results for both detection and recognition
tasks. A comparison of the proposed method with other existing spatio-temporal
feature extraction approaches is also presented.Comment: 21 pages (including references), single column format, accepted to
Signal Processing: Image Communication journa
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