18,103 research outputs found
Learning to Generate Posters of Scientific Papers
Researchers often summarize their work in the form of posters. Posters
provide a coherent and efficient way to convey core ideas from scientific
papers. Generating a good scientific poster, however, is a complex and time
consuming cognitive task, since such posters need to be readable, informative,
and visually aesthetic. In this paper, for the first time, we study the
challenging problem of learning to generate posters from scientific papers. To
this end, a data-driven framework, that utilizes graphical models, is proposed.
Specifically, given content to display, the key elements of a good poster,
including panel layout and attributes of each panel, are learned and inferred
from data. Then, given inferred layout and attributes, composition of graphical
elements within each panel is synthesized. To learn and validate our model, we
collect and make public a Poster-Paper dataset, which consists of scientific
papers and corresponding posters with exhaustively labelled panels and
attributes. Qualitative and quantitative results indicate the effectiveness of
our approach.Comment: in Proceedings of the 30th AAAI Conference on Artificial Intelligence
(AAAI'16), Phoenix, AZ, 201
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression
We present techniques for improving performance driven facial animation,
emotion recognition, and facial key-point or landmark prediction using learned
identity invariant representations. Established approaches to these problems
can work well if sufficient examples and labels for a particular identity are
available and factors of variation are highly controlled. However, labeled
examples of facial expressions, emotions and key-points for new individuals are
difficult and costly to obtain. In this paper we improve the ability of
techniques to generalize to new and unseen individuals by explicitly modeling
previously seen variations related to identity and expression. We use a
weakly-supervised approach in which identity labels are used to learn the
different factors of variation linked to identity separately from factors
related to expression. We show how probabilistic modeling of these sources of
variation allows one to learn identity-invariant representations for
expressions which can then be used to identity-normalize various procedures for
facial expression analysis and animation control. We also show how to extend
the widely used techniques of active appearance models and constrained local
models through replacing the underlying point distribution models which are
typically constructed using principal component analysis with
identity-expression factorized representations. We present a wide variety of
experiments in which we consistently improve performance on emotion
recognition, markerless performance-driven facial animation and facial
key-point tracking.Comment: to appear in Image and Vision Computing Journal (IMAVIS
Automatic Synchronization of Multi-User Photo Galleries
In this paper we address the issue of photo galleries synchronization, where
pictures related to the same event are collected by different users. Existing
solutions to address the problem are usually based on unrealistic assumptions,
like time consistency across photo galleries, and often heavily rely on
heuristics, limiting therefore the applicability to real-world scenarios. We
propose a solution that achieves better generalization performance for the
synchronization task compared to the available literature. The method is
characterized by three stages: at first, deep convolutional neural network
features are used to assess the visual similarity among the photos; then, pairs
of similar photos are detected across different galleries and used to construct
a graph; eventually, a probabilistic graphical model is used to estimate the
temporal offset of each pair of galleries, by traversing the minimum spanning
tree extracted from this graph. The experimental evaluation is conducted on
four publicly available datasets covering different types of events,
demonstrating the strength of our proposed method. A thorough discussion of the
obtained results is provided for a critical assessment of the quality in
synchronization.Comment: ACCEPTED to IEEE Transactions on Multimedi
A graphical model based solution to the facial feature point tracking problem
In this paper a facial feature point tracker that is motivated by applications
such as human-computer interfaces and facial expression analysis systems is
proposed. The proposed tracker is based on a graphical model framework. The
facial features are tracked through video streams by incorporating statistical relations in time as well as spatial relations between feature points. By exploiting the spatial relationships between feature points, the proposed method provides robustness in real-world conditions such as arbitrary head movements and occlusions. A Gabor feature-based occlusion detector is developed and used to handle occlusions. The performance of the proposed tracker has been evaluated
on real video data under various conditions including occluded facial gestures
and head movements. It is also compared to two popular methods, one based
on Kalman filtering exploiting temporal relations, and the other based on active
appearance models (AAM). Improvements provided by the proposed approach
are demonstrated through both visual displays and quantitative analysis
Efficient illumination independent appearance-based face tracking
One of the major challenges that visual tracking algorithms face nowadays is being
able to cope with changes in the appearance of the target during tracking. Linear
subspace models have been extensively studied and are possibly the most popular
way of modelling target appearance. We introduce a linear subspace representation
in which the appearance of a face is represented by the addition of two approxi-
mately independent linear subspaces modelling facial expressions and illumination
respectively. This model is more compact than previous bilinear or multilinear ap-
proaches. The independence assumption notably simplifies system training. We only
require two image sequences. One facial expression is subject to all possible illumina-
tions in one sequence and the face adopts all facial expressions under one particular
illumination in the other. This simple model enables us to train the system with
no manual intervention. We also revisit the problem of efficiently fitting a linear
subspace-based model to a target image and introduce an additive procedure for
solving this problem. We prove that Matthews and Baker’s Inverse Compositional
Approach makes a smoothness assumption on the subspace basis that is equiva-
lent to Hager and Belhumeur’s, which worsens convergence. Our approach differs
from Hager and Belhumeur’s additive and Matthews and Baker’s compositional ap-
proaches in that we make no smoothness assumptions on the subspace basis. In the
experiments conducted we show that the model introduced accurately represents
the appearance variations caused by illumination changes and facial expressions.
We also verify experimentally that our fitting procedure is more accurate and has
better convergence rate than the other related approaches, albeit at the expense of
a slight increase in computational cost. Our approach can be used for tracking a
human face at standard video frame rates on an average personal computer
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