35,994 research outputs found
A Differential Approach for Gaze Estimation
Non-invasive gaze estimation methods usually regress gaze directions directly
from a single face or eye image. However, due to important variabilities in eye
shapes and inner eye structures amongst individuals, universal models obtain
limited accuracies and their output usually exhibit high variance as well as
biases which are subject dependent. Therefore, increasing accuracy is usually
done through calibration, allowing gaze predictions for a subject to be mapped
to his/her actual gaze. In this paper, we introduce a novel image differential
method for gaze estimation. We propose to directly train a differential
convolutional neural network to predict the gaze differences between two eye
input images of the same subject. Then, given a set of subject specific
calibration images, we can use the inferred differences to predict the gaze
direction of a novel eye sample. The assumption is that by allowing the
comparison between two eye images, annoyance factors (alignment, eyelid
closing, illumination perturbations) which usually plague single image
prediction methods can be much reduced, allowing better prediction altogether.
Experiments on 3 public datasets validate our approach which constantly
outperforms state-of-the-art methods even when using only one calibration
sample or when the latter methods are followed by subject specific gaze
adaptation.Comment: Extension to our paper A differential approach for gaze estimation
with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by
PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine
Intelligenc
Mask-guided Style Transfer Network for Purifying Real Images
Recently, the progress of learning-by-synthesis has proposed a training model
for synthetic images, which can effectively reduce the cost of human and
material resources. However, due to the different distribution of synthetic
images compared with real images, the desired performance cannot be achieved.
To solve this problem, the previous method learned a model to improve the
realism of the synthetic images. Different from the previous methods, this
paper try to purify real image by extracting discriminative and robust features
to convert outdoor real images to indoor synthetic images. In this paper, we
first introduce the segmentation masks to construct RGB-mask pairs as inputs,
then we design a mask-guided style transfer network to learn style features
separately from the attention and bkgd(background) regions and learn content
features from full and attention region. Moreover, we propose a novel
region-level task-guided loss to restrain the features learnt from style and
content. Experiments were performed using mixed studies (qualitative and
quantitative) methods to demonstrate the possibility of purifying real images
in complex directions. We evaluate the proposed method on various public
datasets, including LPW, COCO and MPIIGaze. Experimental results show that the
proposed method is effective and achieves the state-of-the-art results.Comment: arXiv admin note: substantial text overlap with arXiv:1903.0582
AVEID: Automatic Video System for Measuring Engagement In Dementia
Engagement in dementia is typically measured using behavior observational
scales (BOS) that are tedious and involve intensive manual labor to annotate,
and are therefore not easily scalable. We propose AVEID, a low cost and
easy-to-use video-based engagement measurement tool to determine the engagement
level of a person with dementia (PwD) during digital interaction. We show that
the objective behavioral measures computed via AVEID correlate well with
subjective expert impressions for the popular MPES and OME BOS, confirming its
viability and effectiveness. Moreover, AVEID measures can be obtained for a
variety of engagement designs, thereby facilitating large-scale studies with
PwD populations
Towards responsive Sensitive Artificial Listeners
This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness
Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images
Analysis-by-synthesis has been a successful approach for many tasks in
computer vision, such as 6D pose estimation of an object in an RGB-D image
which is the topic of this work. The idea is to compare the observation with
the output of a forward process, such as a rendered image of the object of
interest in a particular pose. Due to occlusion or complicated sensor noise, it
can be difficult to perform this comparison in a meaningful way. We propose an
approach that "learns to compare", while taking these difficulties into
account. This is done by describing the posterior density of a particular
object pose with a convolutional neural network (CNN) that compares an observed
and rendered image. The network is trained with the maximum likelihood
paradigm. We observe empirically that the CNN does not specialize to the
geometry or appearance of specific objects, and it can be used with objects of
vastly different shapes and appearances, and in different backgrounds. Compared
to state-of-the-art, we demonstrate a significant improvement on two different
datasets which include a total of eleven objects, cluttered background, and
heavy occlusion.Comment: 16 pages, 8 figure
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