3,340 research outputs found
Kernel Manifold Alignment
We introduce a kernel method for manifold alignment (KEMA) and domain
adaptation that can match an arbitrary number of data sources without needing
corresponding pairs, just few labeled examples in all domains. KEMA has
interesting properties: 1) it generalizes other manifold alignment methods, 2)
it can align manifolds of very different complexities, performing a sort of
manifold unfolding plus alignment, 3) it can define a domain-specific metric to
cope with multimodal specificities, 4) it can align data spaces of different
dimensionality, 5) it is robust to strong nonlinear feature deformations, and
6) it is closed-form invertible which allows transfer across-domains and data
synthesis. We also present a reduced-rank version for computational efficiency
and discuss the generalization performance of KEMA under Rademacher principles
of stability. KEMA exhibits very good performance over competing methods in
synthetic examples, visual object recognition and recognition of facial
expressions tasks
Gaussian process domain experts for model adaptation in facial behavior analysis
We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic fashion by conditioning the target expert on multiple source experts. Furthermore, in contrast to existing adaptation approaches, we also learn a target expert from available target data solely. Then, a single and confident classifier is obtained by combining the predictions from multiple experts based on their confidence. Learning of the model is efficient and requires no retraining/reweighting of the source classifiers. We evaluate the proposed approach on two publicly available datasets for multi-class (MultiPIE) and multi-label (DISFA) facial expression classification. To this end, we perform adaptation of two contextual factors: where (view) and who (subject). We show in our experiments that the proposed approach consistently outperforms both source and target classifiers, while using as few as 30 target examples. It also outperforms the state-of-the-art approaches for supervised domain adaptation
Discriminatively Trained Latent Ordinal Model for Video Classification
We study the problem of video classification for facial analysis and human
action recognition. We propose a novel weakly supervised learning method that
models the video as a sequence of automatically mined, discriminative
sub-events (eg. onset and offset phase for "smile", running and jumping for
"highjump"). The proposed model is inspired by the recent works on Multiple
Instance Learning and latent SVM/HCRF -- it extends such frameworks to model
the ordinal aspect in the videos, approximately. We obtain consistent
improvements over relevant competitive baselines on four challenging and
publicly available video based facial analysis datasets for prediction of
expression, clinical pain and intent in dyadic conversations and on three
challenging human action datasets. We also validate the method with qualitative
results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text
overlap with arXiv:1604.0150
Self-supervised learning of a facial attribute embedding from video
We propose a self-supervised framework for learning facial attributes by
simply watching videos of a human face speaking, laughing, and moving over
time. To perform this task, we introduce a network, Facial Attributes-Net
(FAb-Net), that is trained to embed multiple frames from the same video
face-track into a common low-dimensional space. With this approach, we make
three contributions: first, we show that the network can leverage information
from multiple source frames by predicting confidence/attention masks for each
frame; second, we demonstrate that using a curriculum learning regime improves
the learned embedding; finally, we demonstrate that the network learns a
meaningful face embedding that encodes information about head pose, facial
landmarks and facial expression, i.e. facial attributes, without having been
supervised with any labelled data. We are comparable or superior to
state-of-the-art self-supervised methods on these tasks and approach the
performance of supervised methods.Comment: To appear in BMVC 2018. Supplementary material can be found at
http://www.robots.ox.ac.uk/~vgg/research/unsup_learn_watch_faces/fabnet.htm
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