701 research outputs found
Group Membership Prediction
The group membership prediction (GMP) problem involves predicting whether or
not a collection of instances share a certain semantic property. For instance,
in kinship verification given a collection of images, the goal is to predict
whether or not they share a {\it familial} relationship. In this context we
propose a novel probability model and introduce latent {\em view-specific} and
{\em view-shared} random variables to jointly account for the view-specific
appearance and cross-view similarities among data instances. Our model posits
that data from each view is independent conditioned on the shared variables.
This postulate leads to a parametric probability model that decomposes group
membership likelihood into a tensor product of data-independent parameters and
data-dependent factors. We propose learning the data-independent parameters in
a discriminative way with bilinear classifiers, and test our prediction
algorithm on challenging visual recognition tasks such as multi-camera person
re-identification and kinship verification. On most benchmark datasets, our
method can significantly outperform the current state-of-the-art.Comment: accepted for ICCV 201
KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning
Kinship verification is an emerging task in computer vision with multiple
potential applications. However, there's no large enough kinship dataset to
train a representative and robust model, which is a limitation for achieving
better performance. Moreover, face verification is known to exhibit bias, which
has not been dealt with by previous kinship verification works and sometimes
even results in serious issues. So we first combine existing kinship datasets
and label each identity with the correct race in order to take race information
into consideration and provide a larger and complete dataset, called KinRace
dataset. Secondly, we propose a multi-task learning model structure with
attention module to enhance accuracy, which surpasses state-of-the-art
performance. Lastly, our fairness-aware contrastive loss function with
adversarial learning greatly mitigates racial bias. We introduce a debias term
into traditional contrastive loss and implement gradient reverse in race
classification task, which is an innovative idea to mix two fairness methods to
alleviate bias. Exhaustive experimental evaluation demonstrates the
effectiveness and superior performance of the proposed KFC in both standard
deviation and accuracy at the same time.Comment: Accepted by BMVC 202
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